{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "import json\n",
    "import urllib\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Begin from CSV file\n",
    "http://explore.data.parliament.uk/?endpoint=commonsdivisions#download-list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"downloaded_01May2019.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data cleaning of duplicate motions\n",
    "df['code']= df['_about'].str.extract('(\\d+)', expand=True)\n",
    "df['code'].astype(int)\n",
    "df['motion2'] = df['motion']+' '+df['code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Members and parties\n",
    "metadata = df.drop_duplicates(subset='memberPrinted._value', keep='first')\n",
    "metadata = metadata[['memberParty','memberPrinted._value']]\n",
    "metadata.reset_index(drop=True, inplace=True)\n",
    "#metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(217389, 12)\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>_about</th>\n",
       "      <th>date</th>\n",
       "      <th>member</th>\n",
       "      <th>memberParty</th>\n",
       "      <th>memberPrinted._value</th>\n",
       "      <th>motion</th>\n",
       "      <th>type</th>\n",
       "      <th>vote</th>\n",
       "      <th>vote1</th>\n",
       "      <th>code</th>\n",
       "      <th>motion2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217387</th>\n",
       "      <td>630</td>\n",
       "      <td>http://data.parliament.uk/resources/746031/vot...</td>\n",
       "      <td>2017-06-28</td>\n",
       "      <td>[{'_about': 'http://data.parliament.uk/members...</td>\n",
       "      <td>Labour</td>\n",
       "      <td>Vernon Coaker</td>\n",
       "      <td>Queen's Speech: Health, Social Care and Securi...</td>\n",
       "      <td>http://data.parliament.uk/schema/parl#AyeVote</td>\n",
       "      <td>AyeVote</td>\n",
       "      <td>1</td>\n",
       "      <td>746031</td>\n",
       "      <td>Queen's Speech: Health, Social Care and Securi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217388</th>\n",
       "      <td>631</td>\n",
       "      <td>http://data.parliament.uk/resources/746031/vot...</td>\n",
       "      <td>2017-06-28</td>\n",
       "      <td>[{'_about': 'http://data.parliament.uk/members...</td>\n",
       "      <td>Labour</td>\n",
       "      <td>Mrs Emma Lewell-Buck</td>\n",
       "      <td>Queen's Speech: Health, Social Care and Securi...</td>\n",
       "      <td>http://data.parliament.uk/schema/parl#AyeVote</td>\n",
       "      <td>AyeVote</td>\n",
       "      <td>1</td>\n",
       "      <td>746031</td>\n",
       "      <td>Queen's Speech: Health, Social Care and Securi...</td>\n",
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      ],
      "text/plain": [
       "        Unnamed: 0                                             _about  \\\n",
       "217387         630  http://data.parliament.uk/resources/746031/vot...   \n",
       "217388         631  http://data.parliament.uk/resources/746031/vot...   \n",
       "\n",
       "              date                                             member  \\\n",
       "217387  2017-06-28  [{'_about': 'http://data.parliament.uk/members...   \n",
       "217388  2017-06-28  [{'_about': 'http://data.parliament.uk/members...   \n",
       "\n",
       "       memberParty  memberPrinted._value  \\\n",
       "217387      Labour         Vernon Coaker   \n",
       "217388      Labour  Mrs Emma Lewell-Buck   \n",
       "\n",
       "                                                   motion  \\\n",
       "217387  Queen's Speech: Health, Social Care and Securi...   \n",
       "217388  Queen's Speech: Health, Social Care and Securi...   \n",
       "\n",
       "                                                 type     vote  vote1    code  \\\n",
       "217387  http://data.parliament.uk/schema/parl#AyeVote  AyeVote      1  746031   \n",
       "217388  http://data.parliament.uk/schema/parl#AyeVote  AyeVote      1  746031   \n",
       "\n",
       "                                                  motion2  \n",
       "217387  Queen's Speech: Health, Social Care and Securi...  \n",
       "217388  Queen's Speech: Health, Social Care and Securi...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning for duplicate MPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace({'memberPrinted._value': \\\n",
    "                             {'Mrs Louise Ellman':'Dame Louise Ellman', \\\n",
    "                             'David Evennett':'Sir David Evennett',\\\n",
    "                             'Preet Gill':'Preet Kaur Gill',\\\n",
    "                             'Mrs Cheryl Gillan':'Dame Cheryl Gillan',\\\n",
    "                             'Mr John Hayes':'Sir John Hayes',\\\n",
    "                             'Mr Mark Hendrick':'Sir Mark Hendrick',\\\n",
    "                             'Damian Hinds':'Mr Damian Hinds',\\\n",
    "                             'Mr Nick  Hurd':'Mr Nick Hurd',\\\n",
    "                             'Mr Bernard Jenkin':'Sir Bernard Jenkin',\\\n",
    "                             'Ged Killen':'Gerard Killen',\\\n",
    "                             'Karen Lee':'Ms Karen Lee',\\\n",
    "                             'Caroline  Nokes':'Caroline Nokes',\\\n",
    "                             'Mr Gary Streeter':'Sir Gary Streeter',\\\n",
    "                             'Mr Paul J Sweeney':'Mr Paul Sweeney',\\\n",
    "                             'Mr Robert Syms':'Sir Robert Syms',\\\n",
    "                             'Anne-Marie Trevelyan':'Mrs Anne-Marie Trevelyan',\\\n",
    "                             'Mr Ben  Wallace':'Mr Ben Wallace',\\\n",
    "                             'Mr Alan Campbell':'Sir Alan Campbell',\\\n",
    "                             'Geoffrey Clifton-Brown':'Sir Geoffrey Clifton-Brown',\\\n",
    "                             'Mr Christopher Chope':'Sir Christopher Chope',\\\n",
    "                             'Mike Penning':'Sir Mike Penning',\\\n",
    "                             'Sir Mike  Penning':'Sir Mike Penning',\\\n",
    "                             'Mr Graham Brady':'Sir Graham Brady',\\\n",
    "                             'Sammy  Wilson':'Sammy Wilson',\\\n",
    "                             'Suella Fernandes':'Suella Braverman',\\\n",
    "                             'Julia Dockerill':'Julia Lopez',\\\n",
    "                             'Paul Flynn':'Ruth Jones',\\\n",
    "                             'Heidi Alexander':'Janet Daby'}\\\n",
    "                            })\n",
    "                             \n",
    "#married name: 'Suella Fernandes':'Suella Braverman' \n",
    "#married name: 'Julia Dockerill':'Julia Lopez'\n",
    "#death: 'Paul Flynn':'Ruth Jones' #PaulFlynn died 17Feb2019; succeeded by Ruth Jones\n",
    "#resignation: 'Heidi Alexander':'Janet Daby' #resigned in May 2018 to join the mayoral team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brexit labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Brexit_codelist = pd.read_excel(\"HoC_motions_labels.xlsx\", sheet_name=\"Brexit_codelist\", encoding=\"UTF-8\")\n",
    "Brexit_codelist = Brexit_codelist[\"Brexit related\"].tolist()\n",
    "Brexit_codelist = [str(i) for i in Brexit_codelist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Brexit_df = df[df['code'].isin(Brexit_codelist)]\n",
    "Non_Brexit_df = df[df['code'].isin(Brexit_codelist) == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(639, 414)\n",
      "(639, 192)\n",
      "(639, 222)\n"
     ]
    }
   ],
   "source": [
    "overall = pd.pivot_table(df, values='vote1', index=['memberPrinted._value'], columns=['motion2'], aggfunc=np.sum).fillna(0)\n",
    "print(overall.shape)\n",
    "\n",
    "Brexit_table = pd.pivot_table(Brexit_df, values='vote1', index=['memberPrinted._value'], columns=['motion2'], aggfunc=np.sum).fillna(0)\n",
    "print(Brexit_table.shape)\n",
    "\n",
    "Non_Brexit_table = pd.pivot_table(Non_Brexit_df, values='vote1', index=['memberPrinted._value'], columns=['motion2'], aggfunc=np.sum).fillna(0)\n",
    "print(Non_Brexit_table.shape)\n",
    "\n",
    "MPs = list(overall.index.values) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>motion2</th>\n",
       "      <th>EU (Withdrawal) Act Section 13 Amdt (o) - Blackford 1050639</th>\n",
       "      <th>Agriculture Bill: Second Reading Jeremy Corbyn's Amdt 984695</th>\n",
       "      <th>Air Travel Organisers' Bill: Committee of the whole House Amdt 3 750425</th>\n",
       "      <th>Air Travel Organisers' Licensing Bill: Committee of the whole House Amdt 2 750424</th>\n",
       "      <th>Air Travel Organisers' Licensing Bill: Committee of the whole House New Clause 1 750435</th>\n",
       "      <th>Amendment (a) to Lords Amendment 5 to the EU (Withdrawal) (No. 5) Bill 1110165</th>\n",
       "      <th>Amendment (a) to the Business of the House motion 1109325</th>\n",
       "      <th>Amendment (a) to the motion on the UK's withdrawal from the European Union 1087777</th>\n",
       "      <th>Amendment (a): Privilege (Withdrawal Agreement legal advice) 1019666</th>\n",
       "      <th>Amendment (f) to the motion on the UK's withdrawal from the European Union 1087775</th>\n",
       "      <th>...</th>\n",
       "      <th>draft Immigration (Health Charge) (Amendment) Order 2018 1011382</th>\n",
       "      <th>draft Intellectural Property (Copyright and Related Rights) (Amendment) (EU Exit) Regulations 2018 1060521</th>\n",
       "      <th>draft Long-term Investment Funds (Amendment) (EU Exit) Regulations 2019 1055729</th>\n",
       "      <th>draft Markets in Financial Instruments (Amendment) (EU Exit) Regulations 2018 1028418</th>\n",
       "      <th>draft Official Listing of Securities Prospectus and Transparency (Amendment etc.) (EU Exit) Regulations 2019 1078368</th>\n",
       "      <th>draft Organic Production (Control of Imports) (Amendment) (EU Exit) Regulations 2019 1092056</th>\n",
       "      <th>draft Organic Production and Control (Amendment) (EU Exit) Regulations 2019 1092053</th>\n",
       "      <th>draft Product Safety and Metrology etc. (Amendment etc.) (EU Exit) Regulations 2019 1092054</th>\n",
       "      <th>draft REACH etc. (Amendment etc.) (EU Exit) Regulations 2019 1076721</th>\n",
       "      <th>draft Waste (Miscellaneous Amendments) (EU Exit) Regulations 2019 1087761</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memberPrinted._value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afzal Khan</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Brown</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>Alan Mak</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 414 columns</p>\n",
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      ],
      "text/plain": [
       "motion2                EU (Withdrawal) Act Section 13 Amdt (o) - Blackford 1050639  \\\n",
       "memberPrinted._value                                                                 \n",
       "Adam Afriyie                                                       -1.0              \n",
       "Adam Holloway                                                      -1.0              \n",
       "Afzal Khan                                                          0.0              \n",
       "Alan Brown                                                          1.0              \n",
       "Alan Mak                                                           -1.0              \n",
       "\n",
       "motion2               Agriculture Bill: Second Reading Jeremy Corbyn's Amdt 984695  \\\n",
       "memberPrinted._value                                                                 \n",
       "Adam Afriyie                                                       -1.0              \n",
       "Adam Holloway                                                      -1.0              \n",
       "Afzal Khan                                                          1.0              \n",
       "Alan Brown                                                          0.0              \n",
       "Alan Mak                                                           -1.0              \n",
       "\n",
       "motion2               Air Travel Organisers' Bill: Committee of the whole House Amdt 3 750425  \\\n",
       "memberPrinted._value                                                                            \n",
       "Adam Afriyie                                                       -1.0                         \n",
       "Adam Holloway                                                      -1.0                         \n",
       "Afzal Khan                                                          1.0                         \n",
       "Alan Brown                                                          1.0                         \n",
       "Alan Mak                                                           -1.0                         \n",
       "\n",
       "motion2               Air Travel Organisers' Licensing Bill: Committee of the whole House Amdt 2 750424  \\\n",
       "memberPrinted._value                                                                                      \n",
       "Adam Afriyie                                                       -1.0                                   \n",
       "Adam Holloway                                                      -1.0                                   \n",
       "Afzal Khan                                                          1.0                                   \n",
       "Alan Brown                                                          1.0                                   \n",
       "Alan Mak                                                           -1.0                                   \n",
       "\n",
       "motion2               Air Travel Organisers' Licensing Bill: Committee of the whole House New Clause 1 750435  \\\n",
       "memberPrinted._value                                                                                            \n",
       "Adam Afriyie                                                       -1.0                                         \n",
       "Adam Holloway                                                      -1.0                                         \n",
       "Afzal Khan                                                          1.0                                         \n",
       "Alan Brown                                                          1.0                                         \n",
       "Alan Mak                                                           -1.0                                         \n",
       "\n",
       "motion2               Amendment (a) to Lords Amendment 5 to the EU (Withdrawal) (No. 5) Bill 1110165  \\\n",
       "memberPrinted._value                                                                                   \n",
       "Adam Afriyie                                                        1.0                                \n",
       "Adam Holloway                                                       1.0                                \n",
       "Afzal Khan                                                         -1.0                                \n",
       "Alan Brown                                                         -1.0                                \n",
       "Alan Mak                                                           -1.0                                \n",
       "\n",
       "motion2               Amendment (a) to the Business of the House motion 1109325  \\\n",
       "memberPrinted._value                                                              \n",
       "Adam Afriyie                                                       -1.0           \n",
       "Adam Holloway                                                      -1.0           \n",
       "Afzal Khan                                                          1.0           \n",
       "Alan Brown                                                          1.0           \n",
       "Alan Mak                                                           -1.0           \n",
       "\n",
       "motion2               Amendment (a) to the motion on the UK's withdrawal from the European Union 1087777  \\\n",
       "memberPrinted._value                                                                                       \n",
       "Adam Afriyie                                                       -1.0                                    \n",
       "Adam Holloway                                                      -1.0                                    \n",
       "Afzal Khan                                                          1.0                                    \n",
       "Alan Brown                                                          1.0                                    \n",
       "Alan Mak                                                           -1.0                                    \n",
       "\n",
       "motion2               Amendment (a): Privilege (Withdrawal Agreement legal advice) 1019666  \\\n",
       "memberPrinted._value                                                                         \n",
       "Adam Afriyie                                                        1.0                      \n",
       "Adam Holloway                                                       1.0                      \n",
       "Afzal Khan                                                         -1.0                      \n",
       "Alan Brown                                                         -1.0                      \n",
       "Alan Mak                                                            1.0                      \n",
       "\n",
       "motion2               Amendment (f) to the motion on the UK's withdrawal from the European Union 1087775  \\\n",
       "memberPrinted._value                                                                                       \n",
       "Adam Afriyie                                                        1.0                                    \n",
       "Adam Holloway                                                       1.0                                    \n",
       "Afzal Khan                                                         -1.0                                    \n",
       "Alan Brown                                                         -1.0                                    \n",
       "Alan Mak                                                           -1.0                                    \n",
       "\n",
       "motion2                                                 ...                                      \\\n",
       "memberPrinted._value                                    ...                                       \n",
       "Adam Afriyie                                            ...                                       \n",
       "Adam Holloway                                           ...                                       \n",
       "Afzal Khan                                              ...                                       \n",
       "Alan Brown                                              ...                                       \n",
       "Alan Mak                                                ...                                       \n",
       "\n",
       "motion2               draft Immigration (Health Charge) (Amendment) Order 2018 1011382  \\\n",
       "memberPrinted._value                                                                     \n",
       "Adam Afriyie                                                        1.0                  \n",
       "Adam Holloway                                                       1.0                  \n",
       "Afzal Khan                                                          0.0                  \n",
       "Alan Brown                                                         -1.0                  \n",
       "Alan Mak                                                            1.0                  \n",
       "\n",
       "motion2               draft Intellectural Property (Copyright and Related Rights) (Amendment) (EU Exit) Regulations 2018 1060521  \\\n",
       "memberPrinted._value                                                                                                               \n",
       "Adam Afriyie                                                        1.0                                                            \n",
       "Adam Holloway                                                       1.0                                                            \n",
       "Afzal Khan                                                         -1.0                                                            \n",
       "Alan Brown                                                         -1.0                                                            \n",
       "Alan Mak                                                            1.0                                                            \n",
       "\n",
       "motion2               draft Long-term Investment Funds (Amendment) (EU Exit) Regulations 2019 1055729  \\\n",
       "memberPrinted._value                                                                                    \n",
       "Adam Afriyie                                                        1.0                                 \n",
       "Adam Holloway                                                       1.0                                 \n",
       "Afzal Khan                                                          0.0                                 \n",
       "Alan Brown                                                         -1.0                                 \n",
       "Alan Mak                                                            1.0                                 \n",
       "\n",
       "motion2               draft Markets in Financial Instruments (Amendment) (EU Exit) Regulations 2018 1028418  \\\n",
       "memberPrinted._value                                                                                          \n",
       "Adam Afriyie                                                        1.0                                       \n",
       "Adam Holloway                                                       0.0                                       \n",
       "Afzal Khan                                                         -1.0                                       \n",
       "Alan Brown                                                          0.0                                       \n",
       "Alan Mak                                                            1.0                                       \n",
       "\n",
       "motion2               draft Official Listing of Securities Prospectus and Transparency (Amendment etc.) (EU Exit) Regulations 2019 1078368  \\\n",
       "memberPrinted._value                                                                                                                         \n",
       "Adam Afriyie                                                        1.0                                                                      \n",
       "Adam Holloway                                                       1.0                                                                      \n",
       "Afzal Khan                                                         -1.0                                                                      \n",
       "Alan Brown                                                         -1.0                                                                      \n",
       "Alan Mak                                                            1.0                                                                      \n",
       "\n",
       "motion2               draft Organic Production (Control of Imports) (Amendment) (EU Exit) Regulations 2019 1092056  \\\n",
       "memberPrinted._value                                                                                                 \n",
       "Adam Afriyie                                                        1.0                                              \n",
       "Adam Holloway                                                       0.0                                              \n",
       "Afzal Khan                                                          0.0                                              \n",
       "Alan Brown                                                          0.0                                              \n",
       "Alan Mak                                                            1.0                                              \n",
       "\n",
       "motion2               draft Organic Production and Control (Amendment) (EU Exit) Regulations 2019 1092053  \\\n",
       "memberPrinted._value                                                                                        \n",
       "Adam Afriyie                                                        1.0                                     \n",
       "Adam Holloway                                                       0.0                                     \n",
       "Afzal Khan                                                          0.0                                     \n",
       "Alan Brown                                                         -1.0                                     \n",
       "Alan Mak                                                            1.0                                     \n",
       "\n",
       "motion2               draft Product Safety and Metrology etc. (Amendment etc.) (EU Exit) Regulations 2019 1092054  \\\n",
       "memberPrinted._value                                                                                                \n",
       "Adam Afriyie                                                        1.0                                             \n",
       "Adam Holloway                                                       0.0                                             \n",
       "Afzal Khan                                                         -1.0                                             \n",
       "Alan Brown                                                         -1.0                                             \n",
       "Alan Mak                                                            1.0                                             \n",
       "\n",
       "motion2               draft REACH etc. (Amendment etc.) (EU Exit) Regulations 2019 1076721  \\\n",
       "memberPrinted._value                                                                         \n",
       "Adam Afriyie                                                        1.0                      \n",
       "Adam Holloway                                                       1.0                      \n",
       "Afzal Khan                                                         -1.0                      \n",
       "Alan Brown                                                          0.0                      \n",
       "Alan Mak                                                            1.0                      \n",
       "\n",
       "motion2               draft Waste (Miscellaneous Amendments) (EU Exit) Regulations 2019 1087761  \n",
       "memberPrinted._value                                                                             \n",
       "Adam Afriyie                                                        1.0                          \n",
       "Adam Holloway                                                       1.0                          \n",
       "Afzal Khan                                                         -1.0                          \n",
       "Alan Brown                                                          0.0                          \n",
       "Alan Mak                                                            1.0                          \n",
       "\n",
       "[5 rows x 414 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dot Product - adjacency matrices\n",
    "## No normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def no_normalization(table):\n",
    "    a = np.array(table)\n",
    "    dp = pd.DataFrame(np.dot(a,a.transpose()), columns=MPs, index=MPs).round(3)\n",
    "    #dp.to_csv(\"adjmat-nonormalization.csv\")\n",
    "    return dp.head()\n",
    "\n",
    "#the diagonal should be the max.\n",
    "#i've checked this on excel, looks correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <th>Adam Holloway</th>\n",
       "      <th>Afzal Khan</th>\n",
       "      <th>Alan Brown</th>\n",
       "      <th>Alan Mak</th>\n",
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       "      <th>Alex Burghart</th>\n",
       "      <th>Alex Chalk</th>\n",
       "      <th>...</th>\n",
       "      <th>Victoria Prentis</th>\n",
       "      <th>Wayne David</th>\n",
       "      <th>Wendy Morton</th>\n",
       "      <th>Wera Hobhouse</th>\n",
       "      <th>Wes Streeting</th>\n",
       "      <th>Will Quince</th>\n",
       "      <th>Yasmin Qureshi</th>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <th>Yvonne Fovargue</th>\n",
       "      <th>Zac Goldsmith</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <td>384.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>-293.0</td>\n",
       "      <td>-275.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>-283.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>...</td>\n",
       "      <td>328.0</td>\n",
       "      <td>-294.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>-264.0</td>\n",
       "      <td>-296.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>-259.0</td>\n",
       "      <td>-278.0</td>\n",
       "      <td>-255.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>327.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>-257.0</td>\n",
       "      <td>-245.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>-248.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>...</td>\n",
       "      <td>290.0</td>\n",
       "      <td>-260.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>-242.0</td>\n",
       "      <td>-262.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>-234.0</td>\n",
       "      <td>-247.0</td>\n",
       "      <td>-220.0</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afzal Khan</th>\n",
       "      <td>-293.0</td>\n",
       "      <td>-257.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>-275.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>-251.0</td>\n",
       "      <td>-246.0</td>\n",
       "      <td>-280.0</td>\n",
       "      <td>-270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-276.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>-235.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>-279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Brown</th>\n",
       "      <td>-275.0</td>\n",
       "      <td>-245.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>-267.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>-245.0</td>\n",
       "      <td>-246.0</td>\n",
       "      <td>-269.0</td>\n",
       "      <td>-259.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-267.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>-231.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>-270.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>-268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Mak</th>\n",
       "      <td>346.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>-275.0</td>\n",
       "      <td>-267.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>-271.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>349.0</td>\n",
       "      <td>-277.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>-250.0</td>\n",
       "      <td>-280.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>-245.0</td>\n",
       "      <td>-263.0</td>\n",
       "      <td>-245.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 639 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Adam Afriyie  Adam Holloway  Afzal Khan  Alan Brown  Alan Mak  \\\n",
       "Adam Afriyie          384.0          327.0      -293.0      -275.0     346.0   \n",
       "Adam Holloway         327.0          339.0      -257.0      -245.0     302.0   \n",
       "Afzal Khan           -293.0         -257.0       346.0       253.0    -275.0   \n",
       "Alan Brown           -275.0         -245.0       253.0       302.0    -267.0   \n",
       "Alan Mak              346.0          302.0      -275.0      -267.0     380.0   \n",
       "\n",
       "               Albert Owen  Alberto Costa  Alec Shelbrooke  Alex Burghart  \\\n",
       "Adam Afriyie        -283.0          316.0            305.0          348.0   \n",
       "Adam Holloway       -248.0          275.0            265.0          306.0   \n",
       "Afzal Khan           308.0         -251.0           -246.0         -280.0   \n",
       "Alan Brown           248.0         -245.0           -246.0         -269.0   \n",
       "Alan Mak            -271.0          342.0            319.0          369.0   \n",
       "\n",
       "               Alex Chalk      ...        Victoria Prentis  Wayne David  \\\n",
       "Adam Afriyie        333.0      ...                   328.0       -294.0   \n",
       "Adam Holloway       295.0      ...                   290.0       -260.0   \n",
       "Afzal Khan         -270.0      ...                  -276.0        334.0   \n",
       "Alan Brown         -259.0      ...                  -267.0        253.0   \n",
       "Alan Mak            361.0      ...                   349.0       -277.0   \n",
       "\n",
       "               Wendy Morton  Wera Hobhouse  Wes Streeting  Will Quince  \\\n",
       "Adam Afriyie          306.0         -264.0         -296.0        359.0   \n",
       "Adam Holloway         261.0         -242.0         -262.0        315.0   \n",
       "Afzal Khan           -235.0          274.0          329.0       -279.0   \n",
       "Alan Brown           -231.0          248.0          263.0       -270.0   \n",
       "Alan Mak              321.0         -250.0         -280.0        363.0   \n",
       "\n",
       "               Yasmin Qureshi  Yvette Cooper  Yvonne Fovargue  Zac Goldsmith  \n",
       "Adam Afriyie           -259.0         -278.0           -255.0          359.0  \n",
       "Adam Holloway          -234.0         -247.0           -220.0          320.0  \n",
       "Afzal Khan              291.0          313.0            289.0         -279.0  \n",
       "Alan Brown              237.0          246.0            221.0         -268.0  \n",
       "Alan Mak               -245.0         -263.0           -245.0          343.0  \n",
       "\n",
       "[5 rows x 639 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_normalization(overall)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jaccard Similarity\n",
    "### Arithmetic mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import pairwise_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jaccard_similarity(table):\n",
    "    am = pd.DataFrame(1-pairwise_distances(table, metric =\"jaccard\"), columns=MPs, index=MPs).round(3)\n",
    "    return am.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype float64 was converted to bool by check_pairwise_arrays.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <th>Adam Holloway</th>\n",
       "      <th>Afzal Khan</th>\n",
       "      <th>Alan Brown</th>\n",
       "      <th>Alan Mak</th>\n",
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       "      <th>Alex Burghart</th>\n",
       "      <th>Alex Chalk</th>\n",
       "      <th>...</th>\n",
       "      <th>Victoria Prentis</th>\n",
       "      <th>Wayne David</th>\n",
       "      <th>Wendy Morton</th>\n",
       "      <th>Wera Hobhouse</th>\n",
       "      <th>Wes Streeting</th>\n",
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       "      <th>Yasmin Qureshi</th>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <th>Yvonne Fovargue</th>\n",
       "      <th>Zac Goldsmith</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.937</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>0.854</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.838</td>\n",
       "      <td>...</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afzal Khan</th>\n",
       "      <td>0.802</td>\n",
       "      <td>0.730</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.819</td>\n",
       "      <td>...</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Brown</th>\n",
       "      <td>0.728</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.666</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.740</td>\n",
       "      <td>...</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Mak</th>\n",
       "      <td>0.939</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.735</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.947</td>\n",
       "      <td>...</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 639 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Adam Afriyie  Adam Holloway  Afzal Khan  Alan Brown  Alan Mak  \\\n",
       "Adam Afriyie          1.000          0.854       0.802       0.728     0.939   \n",
       "Adam Holloway         0.854          1.000       0.730       0.687     0.820   \n",
       "Afzal Khan            0.802          0.730       1.000       0.666     0.793   \n",
       "Alan Brown            0.728          0.687       0.666       1.000     0.735   \n",
       "Alan Mak              0.939          0.820       0.793       0.735     1.000   \n",
       "\n",
       "               Albert Owen  Alberto Costa  Alec Shelbrooke  Alex Burghart  \\\n",
       "Adam Afriyie         0.782          0.909            0.829          0.949   \n",
       "Adam Holloway        0.704          0.805            0.728          0.840   \n",
       "Afzal Khan           0.839          0.782            0.728          0.807   \n",
       "Alan Brown           0.665          0.706            0.702          0.746   \n",
       "Alan Mak             0.781          0.918            0.828          0.944   \n",
       "\n",
       "               Alex Chalk      ...        Victoria Prentis  Wayne David  \\\n",
       "Adam Afriyie        0.937      ...                   0.898        0.799   \n",
       "Adam Holloway       0.838      ...                   0.799        0.726   \n",
       "Afzal Khan          0.819      ...                   0.794        0.946   \n",
       "Alan Brown          0.740      ...                   0.732        0.674   \n",
       "Alan Mak            0.947      ...                   0.912        0.785   \n",
       "\n",
       "               Wendy Morton  Wera Hobhouse  Wes Streeting  Will Quince  \\\n",
       "Adam Afriyie          0.808          0.802          0.816        0.942   \n",
       "Adam Holloway         0.704          0.756          0.743        0.835   \n",
       "Afzal Khan            0.700          0.778          0.917        0.802   \n",
       "Alan Brown            0.649          0.674          0.701        0.725   \n",
       "Alan Mak              0.812          0.792          0.797        0.923   \n",
       "\n",
       "               Yasmin Qureshi  Yvette Cooper  Yvonne Fovargue  Zac Goldsmith  \n",
       "Adam Afriyie            0.725          0.758            0.733          0.931  \n",
       "Adam Holloway           0.688          0.705            0.661          0.864  \n",
       "Afzal Khan              0.832          0.884            0.831          0.780  \n",
       "Alan Brown              0.651          0.653            0.616          0.718  \n",
       "Alan Mak                0.698          0.752            0.714          0.902  \n",
       "\n",
       "[5 rows x 639 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jaccard_similarity(overall)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "It makes sense. Jaccard formula is the number of common attributes (intersection) is divided by the number of attributes that exist in at least one of the two objects (union). There are only two, values -1 and 1, so the value will be high.  Cosine similarity is for comparing two real-valued vectors, but Jaccard similarity is for comparing two binary vectors (sets). Jaccard similarity is good for cases where duplication does not matter, cosine similarity is good for cases where duplication matters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cosine Similarity\n",
    "### Geometric mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_similarity(table):\n",
    "    cs = pd.DataFrame(1-pairwise_distances(table, metric =\"cosine\"), columns=MPs, index=MPs).round(3)\n",
    "    iu1 = np.triu_indices(639) \n",
    "    triangle_upper = cs.values[iu1]\n",
    "    plt.hist(triangle_upper, range=[-1, 1], bins=\"auto\")\n",
    "    plt.show()\n",
    "    #cs.to_csv(\"adjmat.csv\")\n",
    "    return cs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <th>Adam Holloway</th>\n",
       "      <th>Afzal Khan</th>\n",
       "      <th>Alan Brown</th>\n",
       "      <th>Alan Mak</th>\n",
       "      <th>Albert Owen</th>\n",
       "      <th>Alberto Costa</th>\n",
       "      <th>Alec Shelbrooke</th>\n",
       "      <th>Alex Burghart</th>\n",
       "      <th>Alex Chalk</th>\n",
       "      <th>...</th>\n",
       "      <th>Victoria Prentis</th>\n",
       "      <th>Wayne David</th>\n",
       "      <th>Wendy Morton</th>\n",
       "      <th>Wera Hobhouse</th>\n",
       "      <th>Wes Streeting</th>\n",
       "      <th>Will Quince</th>\n",
       "      <th>Yasmin Qureshi</th>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <th>Yvonne Fovargue</th>\n",
       "      <th>Zac Goldsmith</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.906</td>\n",
       "      <td>-0.804</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>0.906</td>\n",
       "      <td>-0.796</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.862</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>0.846</td>\n",
       "      <td>-0.726</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>0.924</td>\n",
       "      <td>-0.764</td>\n",
       "      <td>-0.792</td>\n",
       "      <td>-0.746</td>\n",
       "      <td>0.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>0.906</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.750</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>0.841</td>\n",
       "      <td>-0.743</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.811</td>\n",
       "      <td>-0.765</td>\n",
       "      <td>0.768</td>\n",
       "      <td>-0.709</td>\n",
       "      <td>-0.765</td>\n",
       "      <td>0.863</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>-0.749</td>\n",
       "      <td>-0.685</td>\n",
       "      <td>0.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afzal Khan</th>\n",
       "      <td>-0.804</td>\n",
       "      <td>-0.750</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.783</td>\n",
       "      <td>-0.758</td>\n",
       "      <td>0.913</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-0.725</td>\n",
       "      <td>-0.768</td>\n",
       "      <td>-0.738</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.764</td>\n",
       "      <td>0.972</td>\n",
       "      <td>-0.684</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.951</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.891</td>\n",
       "      <td>-0.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Brown</th>\n",
       "      <td>-0.808</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>0.783</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.788</td>\n",
       "      <td>0.787</td>\n",
       "      <td>-0.731</td>\n",
       "      <td>-0.776</td>\n",
       "      <td>-0.790</td>\n",
       "      <td>-0.758</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.791</td>\n",
       "      <td>0.788</td>\n",
       "      <td>-0.720</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.814</td>\n",
       "      <td>-0.784</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.729</td>\n",
       "      <td>-0.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Mak</th>\n",
       "      <td>0.906</td>\n",
       "      <td>0.841</td>\n",
       "      <td>-0.758</td>\n",
       "      <td>-0.788</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.922</td>\n",
       "      <td>-0.770</td>\n",
       "      <td>0.892</td>\n",
       "      <td>-0.691</td>\n",
       "      <td>-0.772</td>\n",
       "      <td>0.939</td>\n",
       "      <td>-0.727</td>\n",
       "      <td>-0.753</td>\n",
       "      <td>-0.721</td>\n",
       "      <td>0.911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 639 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Adam Afriyie  Adam Holloway  Afzal Khan  Alan Brown  Alan Mak  \\\n",
       "Adam Afriyie          1.000          0.906      -0.804      -0.808     0.906   \n",
       "Adam Holloway         0.906          1.000      -0.750      -0.766     0.841   \n",
       "Afzal Khan           -0.804         -0.750       1.000       0.783    -0.758   \n",
       "Alan Brown           -0.808         -0.766       0.783       1.000    -0.788   \n",
       "Alan Mak              0.906          0.841      -0.758      -0.788     1.000   \n",
       "\n",
       "               Albert Owen  Alberto Costa  Alec Shelbrooke  Alex Burghart  \\\n",
       "Adam Afriyie        -0.796          0.836            0.853          0.906   \n",
       "Adam Holloway       -0.743          0.774            0.789          0.848   \n",
       "Afzal Khan           0.913         -0.700           -0.725         -0.768   \n",
       "Alan Brown           0.787         -0.731           -0.776         -0.790   \n",
       "Alan Mak            -0.766          0.910            0.897          0.966   \n",
       "\n",
       "               Alex Chalk      ...        Victoria Prentis  Wayne David  \\\n",
       "Adam Afriyie        0.864      ...                   0.862       -0.812   \n",
       "Adam Holloway       0.814      ...                   0.811       -0.765   \n",
       "Afzal Khan         -0.738      ...                  -0.764        0.972   \n",
       "Alan Brown         -0.758      ...                  -0.791        0.788   \n",
       "Alan Mak            0.941      ...                   0.922       -0.770   \n",
       "\n",
       "               Wendy Morton  Wera Hobhouse  Wes Streeting  Will Quince  \\\n",
       "Adam Afriyie          0.846         -0.726         -0.812        0.924   \n",
       "Adam Holloway         0.768         -0.709         -0.765        0.863   \n",
       "Afzal Khan           -0.684          0.794          0.951       -0.757   \n",
       "Alan Brown           -0.720          0.769          0.814       -0.784   \n",
       "Alan Mak              0.892         -0.691         -0.772        0.939   \n",
       "\n",
       "               Yasmin Qureshi  Yvette Cooper  Yvonne Fovargue  Zac Goldsmith  \n",
       "Adam Afriyie           -0.764         -0.792           -0.746          0.949  \n",
       "Adam Holloway          -0.735         -0.749           -0.685          0.900  \n",
       "Afzal Khan              0.905          0.939            0.891         -0.777  \n",
       "Alan Brown              0.789          0.790            0.729         -0.799  \n",
       "Alan Mak               -0.727         -0.753           -0.721          0.911  \n",
       "\n",
       "[5 rows x 639 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarity(overall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <th>Adam Holloway</th>\n",
       "      <th>Afzal Khan</th>\n",
       "      <th>Alan Brown</th>\n",
       "      <th>Alan Mak</th>\n",
       "      <th>Albert Owen</th>\n",
       "      <th>Alberto Costa</th>\n",
       "      <th>Alec Shelbrooke</th>\n",
       "      <th>Alex Burghart</th>\n",
       "      <th>Alex Chalk</th>\n",
       "      <th>...</th>\n",
       "      <th>Victoria Prentis</th>\n",
       "      <th>Wayne David</th>\n",
       "      <th>Wendy Morton</th>\n",
       "      <th>Wera Hobhouse</th>\n",
       "      <th>Wes Streeting</th>\n",
       "      <th>Will Quince</th>\n",
       "      <th>Yasmin Qureshi</th>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <th>Yvonne Fovargue</th>\n",
       "      <th>Zac Goldsmith</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.915</td>\n",
       "      <td>-0.864</td>\n",
       "      <td>-0.870</td>\n",
       "      <td>0.850</td>\n",
       "      <td>-0.888</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.773</td>\n",
       "      <td>...</td>\n",
       "      <td>0.811</td>\n",
       "      <td>-0.861</td>\n",
       "      <td>0.825</td>\n",
       "      <td>-0.798</td>\n",
       "      <td>-0.871</td>\n",
       "      <td>0.913</td>\n",
       "      <td>-0.848</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>-0.768</td>\n",
       "      <td>0.947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>0.915</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>-0.831</td>\n",
       "      <td>0.779</td>\n",
       "      <td>-0.835</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.710</td>\n",
       "      <td>...</td>\n",
       "      <td>0.737</td>\n",
       "      <td>-0.809</td>\n",
       "      <td>0.735</td>\n",
       "      <td>-0.767</td>\n",
       "      <td>-0.816</td>\n",
       "      <td>0.851</td>\n",
       "      <td>-0.810</td>\n",
       "      <td>-0.799</td>\n",
       "      <td>-0.711</td>\n",
       "      <td>0.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afzal Khan</th>\n",
       "      <td>-0.864</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.886</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>0.935</td>\n",
       "      <td>-0.679</td>\n",
       "      <td>-0.791</td>\n",
       "      <td>-0.814</td>\n",
       "      <td>-0.750</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.772</td>\n",
       "      <td>0.985</td>\n",
       "      <td>-0.747</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.964</td>\n",
       "      <td>-0.822</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.900</td>\n",
       "      <td>-0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Brown</th>\n",
       "      <td>-0.870</td>\n",
       "      <td>-0.831</td>\n",
       "      <td>0.886</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.820</td>\n",
       "      <td>0.906</td>\n",
       "      <td>-0.716</td>\n",
       "      <td>-0.811</td>\n",
       "      <td>-0.821</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.813</td>\n",
       "      <td>0.882</td>\n",
       "      <td>-0.743</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.911</td>\n",
       "      <td>-0.829</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.795</td>\n",
       "      <td>-0.873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Mak</th>\n",
       "      <td>0.850</td>\n",
       "      <td>0.779</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>-0.820</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.845</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.906</td>\n",
       "      <td>-0.805</td>\n",
       "      <td>0.912</td>\n",
       "      <td>-0.741</td>\n",
       "      <td>-0.816</td>\n",
       "      <td>0.928</td>\n",
       "      <td>-0.789</td>\n",
       "      <td>-0.790</td>\n",
       "      <td>-0.749</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 639 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Adam Afriyie  Adam Holloway  Afzal Khan  Alan Brown  Alan Mak  \\\n",
       "Adam Afriyie          1.000          0.915      -0.864      -0.870     0.850   \n",
       "Adam Holloway         0.915          1.000      -0.812      -0.831     0.779   \n",
       "Afzal Khan           -0.864         -0.812       1.000       0.886    -0.808   \n",
       "Alan Brown           -0.870         -0.831       0.886       1.000    -0.820   \n",
       "Alan Mak              0.850          0.779      -0.808      -0.820     1.000   \n",
       "\n",
       "               Albert Owen  Alberto Costa  Alec Shelbrooke  Alex Burghart  \\\n",
       "Adam Afriyie        -0.888          0.715            0.806          0.854   \n",
       "Adam Holloway       -0.835          0.631            0.736          0.786   \n",
       "Afzal Khan           0.935         -0.679           -0.791         -0.814   \n",
       "Alan Brown           0.906         -0.716           -0.811         -0.821   \n",
       "Alan Mak            -0.845          0.837            0.888          0.965   \n",
       "\n",
       "               Alex Chalk      ...        Victoria Prentis  Wayne David  \\\n",
       "Adam Afriyie        0.773      ...                   0.811       -0.861   \n",
       "Adam Holloway       0.710      ...                   0.737       -0.809   \n",
       "Afzal Khan         -0.750      ...                  -0.772        0.985   \n",
       "Alan Brown         -0.757      ...                  -0.813        0.882   \n",
       "Alan Mak            0.905      ...                   0.906       -0.805   \n",
       "\n",
       "               Wendy Morton  Wera Hobhouse  Wes Streeting  Will Quince  \\\n",
       "Adam Afriyie          0.825         -0.798         -0.871        0.913   \n",
       "Adam Holloway         0.735         -0.767         -0.816        0.851   \n",
       "Afzal Khan           -0.747          0.838          0.964       -0.822   \n",
       "Alan Brown           -0.743          0.911          0.911       -0.829   \n",
       "Alan Mak              0.912         -0.741         -0.816        0.928   \n",
       "\n",
       "               Yasmin Qureshi  Yvette Cooper  Yvonne Fovargue  Zac Goldsmith  \n",
       "Adam Afriyie           -0.848         -0.843           -0.768          0.947  \n",
       "Adam Holloway          -0.810         -0.799           -0.711          0.901  \n",
       "Afzal Khan              0.966          0.966            0.900         -0.855  \n",
       "Alan Brown              0.880          0.874            0.795         -0.873  \n",
       "Alan Mak               -0.789         -0.790           -0.749          0.896  \n",
       "\n",
       "[5 rows x 639 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarity(Brexit_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TVTV5kvtj9K6IeiNwV7tJkhbR0HCoqkunmHXegLEFXDHFerYD2wfUx4B3DetDkrRw/G0lSVKH4SBJ6jAcJEkdhoMkqcNwkCR1GA6SpA7DQZLUYThIkjoMB0lSh+EgSeowHCRJHYaDJKnDcJAkdRgOkqSOoT/ZLUnHkzXb7lzsFpYE9xwkSR3uOUh6TRn2yf+Zaz+wQJ0c29xzkCR1GA6SpA7DQZLUMatwSPJMkkeTPJxkrNVOSbInyb52v7zVk+SGJONJHklyVt96trTx+5Jsmd1LkiTN1lyckH5fVT3f93gbcHdVXZtkW3v8ceBCYG27nQPcCJyT5BTgamA9UMBDSXZW1Qtz0Jsk/S1eqjqa+TistAnY0aZ3ABf31W+pnvuAk5OsBC4A9lTV4RYIe4CN89CXJGlEsw2HAv53koeSbG21t1XVAYB2/9ZWXwU827fsRKtNVe9IsjXJWJKxQ4cOzbJ1SdJUZntY6T1VtT/JW4E9Sf7yKGMzoFZHqXeLVTcBNwGsX79+4BhJ0uzNas+hqva3+4PAV4Czgefa4SLa/cE2fAI4rW/x1cD+o9QlSYtkxuGQ5O8m+XuT08D5wGPATmDyiqMtwB1teidwWbtqaQPwUjvstBs4P8nydmXT+a0mSVokszms9DbgK0km1/M/qup/JXkQuC3J5cB3gQ+38buAi4Bx4EfARwCq6nCSTwMPtnGfqqrDs+hLQ/jzApKGmXE4VNVTwC8NqP8VcN6AegFXTLGu7cD2mfYiSZpbfkNaktRhOEiSOvzJbknHFL/hvDDcc5AkdRgOkqQOw0GS1GE4SJI6DAdJUofhIEnqMBwkSR1+z2EeeB22pGOdew6SpA73HNThr7ZqMbnnvTS45yBJ6jAcJEkdhoMkqcNwkCR1eEJa0+YJa82GJ5yPDe45SJI6lkw4JNmY5Mkk40m2LXY/knQ8WxKHlZIsAz4L/HNgAngwyc6qenxxO9NMeNjp+OZho9eGpbLncDYwXlVPVdWPgVuBTYvckyQdt5bEngOwCni27/EEcM4i9TKUn4xmZ773LGb7/hzrezb+fWouLJVwyIBadQYlW4Gt7eEPkzw5w+c7FXh+hsvOJ/sCct3IQ+elr2k8/1R8H6fHvqYh1826r58bZdBSCYcJ4LS+x6uB/UcOqqqbgJtm+2RJxqpq/WzXM9fsa3rsa3rsa3qO976WyjmHB4G1SU5PciKwGdi5yD1J0nFrSew5VNUrSa4EdgPLgO1VtXeR25Kk49aSCAeAqtoF7Fqgp5v1oal5Yl/TY1/TY1/Tc1z3larOeV9J0nFuqZxzkCQtIa/ZcEjy4SR7k/wkyZRn9qf62Y52cvz+JPuSfKmdKJ+Lvk5Jsqetd0+S5QPGvC/Jw323v05ycZt3c5Kn++atW6i+2rhX+557Z199MbfXuiTfaO/3I0n+Vd+8Od1ew37mJclJ7fWPt+2xpm/eVa3+ZJILZtPHDPr690keb9vn7iQ/1zdv4Hu6QH39WpJDfc//b/rmbWnv+74kWxa4r+v7evp2khf75s3L9kqyPcnBJI9NMT9Jbmg9P5LkrL55c7+tquo1eQPeAfwC8FVg/RRjlgHfAd4OnAh8CzijzbsN2Nym/wD42Bz19V+AbW16G3DdkPGnAIeBv9Me3wxcMg/ba6S+gB9OUV+07QX8I2Btm/5Z4ABw8lxvr6P9vfSN+bfAH7TpzcCX2vQZbfxJwOltPcsWsK/39f0NfWyyr6O9pwvU168Bvz9g2VOAp9r98ja9fKH6OmL8v6N3kcx8b69/BpwFPDbF/IuAu+h9L2wDcP98bqvX7J5DVT1RVcO+JDfwZzuSBDgXuL2N2wFcPEetbWrrG3W9lwB3VdWP5uj5pzLdvv6/xd5eVfXtqtrXpvcDB4EVc/T8/Ub5mZf+fm8HzmvbZxNwa1W9XFVPA+NtfQvSV1Xd2/c3dB+97xLNt9n8LM4FwJ6qOlxVLwB7gI2L1NelwBfn6LmnVFVfo/dBcCqbgFuq5z7g5CQrmadt9ZoNhxEN+tmOVcBbgBer6pUj6nPhbVV1AKDdv3XI+M10/zCvabuV1yc5aYH7ekOSsST3TR7qYgltryRn0/s0+J2+8lxtr6n+XgaOadvjJXrbZ5Rl57OvfpfT+wQ6adB7upB9/Yv2/tyeZPLLsEtie7XDb6cD9/SV52t7DTNV3/OyrZbMpawzkeTPgb8/YNYnquqOUVYxoFZHqc+6r1HX0dazEvjH9L7/Mekq4P/S+wfwJuDjwKcWsK9/UFX7k7wduCfJo8D3B4xbrO31R8CWqvpJK894ew16igG1I1/nvPxNDTHyupP8CrAe+OW+cuc9rarvDFp+Hvr6n8AXq+rlJB+lt9d17ojLzmdfkzYDt1fVq321+dpewyzo39YxHQ5V9f5ZrmKqn+14nt4u2wnt09/An/OYSV9JnkuysqoOtH/MDh5lVf8S+EpV/U3fug+0yZeT/CHwHxayr3bYhqp6KslXgTOBL7PI2yvJm4E7gf/Udrkn1z3j7TXAKD/zMjlmIskJwM/QO1Qw0k/EzGNfJHk/vcD95ap6ebI+xXs6F//YDe2rqv6q7+HngclftpoA3nvEsl+dg55G6qvPZuCK/sI8bq9hpup7XrbV8X5YaeDPdlTvLM+99I73A2wBRtkTGcXOtr5R1ts51tn+gZw8zn8xMPDKhvnoK8nyycMySU4F3gM8vtjbq713X6F3PPZPjpg3l9trlJ956e/3EuCetn12ApvTu5rpdGAt8MAseplWX0nOBP4r8MGqOthXH/ieLmBfK/sefhB4ok3vBs5v/S0Hzudv70HPa1+tt1+gd4L3G321+dxew+wELmtXLW0AXmoffuZnW83HWfelcAM+RC9RXwaeA3a3+s8Cu/rGXQR8m17yf6Kv/nZ6//GOA38CnDRHfb0FuBvY1+5PafX1wH/rG7cG+B7wuiOWvwd4lN4/cv8deNNC9QX80/bc32r3ly+F7QX8CvA3wMN9t3Xzsb0G/b3QO0z1wTb9hvb6x9v2eHvfsp9oyz0JXDjHf+/D+vrz9t/B5PbZOew9XaC+/jOwtz3/vcAv9i37r9t2HAc+spB9tcefBK49Yrl52170PggeaH/LE/TODX0U+GibH3r/U7TvtOde37fsnG8rvyEtSeo43g8rSZIGMBwkSR2GgySpw3CQJHUYDpKkDsNBktRhOEiSOgwHSVLH/wNn41VJakKeXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <th>Adam Holloway</th>\n",
       "      <th>Afzal Khan</th>\n",
       "      <th>Alan Brown</th>\n",
       "      <th>Alan Mak</th>\n",
       "      <th>Albert Owen</th>\n",
       "      <th>Alberto Costa</th>\n",
       "      <th>Alec Shelbrooke</th>\n",
       "      <th>Alex Burghart</th>\n",
       "      <th>Alex Chalk</th>\n",
       "      <th>...</th>\n",
       "      <th>Victoria Prentis</th>\n",
       "      <th>Wayne David</th>\n",
       "      <th>Wendy Morton</th>\n",
       "      <th>Wera Hobhouse</th>\n",
       "      <th>Wes Streeting</th>\n",
       "      <th>Will Quince</th>\n",
       "      <th>Yasmin Qureshi</th>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <th>Yvonne Fovargue</th>\n",
       "      <th>Zac Goldsmith</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.898</td>\n",
       "      <td>-0.747</td>\n",
       "      <td>-0.742</td>\n",
       "      <td>0.959</td>\n",
       "      <td>-0.701</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.911</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>0.865</td>\n",
       "      <td>-0.657</td>\n",
       "      <td>-0.754</td>\n",
       "      <td>0.935</td>\n",
       "      <td>-0.678</td>\n",
       "      <td>-0.742</td>\n",
       "      <td>-0.724</td>\n",
       "      <td>0.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>0.898</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.693</td>\n",
       "      <td>-0.698</td>\n",
       "      <td>0.900</td>\n",
       "      <td>-0.649</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.912</td>\n",
       "      <td>...</td>\n",
       "      <td>0.880</td>\n",
       "      <td>-0.724</td>\n",
       "      <td>0.799</td>\n",
       "      <td>-0.653</td>\n",
       "      <td>-0.716</td>\n",
       "      <td>0.875</td>\n",
       "      <td>-0.659</td>\n",
       "      <td>-0.701</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>0.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afzal Khan</th>\n",
       "      <td>-0.747</td>\n",
       "      <td>-0.693</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.678</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0.893</td>\n",
       "      <td>-0.718</td>\n",
       "      <td>-0.663</td>\n",
       "      <td>-0.726</td>\n",
       "      <td>-0.727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>0.961</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.938</td>\n",
       "      <td>-0.696</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.883</td>\n",
       "      <td>-0.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Brown</th>\n",
       "      <td>-0.742</td>\n",
       "      <td>-0.698</td>\n",
       "      <td>0.678</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.758</td>\n",
       "      <td>0.648</td>\n",
       "      <td>-0.752</td>\n",
       "      <td>-0.742</td>\n",
       "      <td>-0.760</td>\n",
       "      <td>-0.763</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.773</td>\n",
       "      <td>0.692</td>\n",
       "      <td>-0.698</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.709</td>\n",
       "      <td>-0.739</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.658</td>\n",
       "      <td>-0.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Mak</th>\n",
       "      <td>0.959</td>\n",
       "      <td>0.900</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>-0.758</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.688</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.975</td>\n",
       "      <td>...</td>\n",
       "      <td>0.937</td>\n",
       "      <td>-0.737</td>\n",
       "      <td>0.873</td>\n",
       "      <td>-0.645</td>\n",
       "      <td>-0.731</td>\n",
       "      <td>0.950</td>\n",
       "      <td>-0.666</td>\n",
       "      <td>-0.718</td>\n",
       "      <td>-0.694</td>\n",
       "      <td>0.925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 639 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Adam Afriyie  Adam Holloway  Afzal Khan  Alan Brown  Alan Mak  \\\n",
       "Adam Afriyie          1.000          0.898      -0.747      -0.742     0.959   \n",
       "Adam Holloway         0.898          1.000      -0.693      -0.698     0.900   \n",
       "Afzal Khan           -0.747         -0.693       1.000       0.678    -0.713   \n",
       "Alan Brown           -0.742         -0.698       0.678       1.000    -0.758   \n",
       "Alan Mak              0.959          0.900      -0.713      -0.758     1.000   \n",
       "\n",
       "               Albert Owen  Alberto Costa  Alec Shelbrooke  Alex Burghart  \\\n",
       "Adam Afriyie        -0.701          0.949            0.898          0.957   \n",
       "Adam Holloway       -0.649          0.905            0.838          0.907   \n",
       "Afzal Khan           0.893         -0.718           -0.663         -0.726   \n",
       "Alan Brown           0.648         -0.752           -0.742         -0.760   \n",
       "Alan Mak            -0.688          0.975            0.905          0.967   \n",
       "\n",
       "               Alex Chalk      ...        Victoria Prentis  Wayne David  \\\n",
       "Adam Afriyie        0.950      ...                   0.911       -0.766   \n",
       "Adam Holloway       0.912      ...                   0.880       -0.724   \n",
       "Afzal Khan         -0.727      ...                  -0.757        0.961   \n",
       "Alan Brown         -0.763      ...                  -0.773        0.692   \n",
       "Alan Mak            0.975      ...                   0.937       -0.737   \n",
       "\n",
       "               Wendy Morton  Wera Hobhouse  Wes Streeting  Will Quince  \\\n",
       "Adam Afriyie          0.865         -0.657         -0.754        0.935   \n",
       "Adam Holloway         0.799         -0.653         -0.716        0.875   \n",
       "Afzal Khan           -0.625          0.753          0.938       -0.696   \n",
       "Alan Brown           -0.698          0.618          0.709       -0.739   \n",
       "Alan Mak              0.873         -0.645         -0.731        0.950   \n",
       "\n",
       "               Yasmin Qureshi  Yvette Cooper  Yvonne Fovargue  Zac Goldsmith  \n",
       "Adam Afriyie           -0.678         -0.742           -0.724          0.950  \n",
       "Adam Holloway          -0.659         -0.701           -0.660          0.899  \n",
       "Afzal Khan              0.845          0.914            0.883         -0.703  \n",
       "Alan Brown              0.684          0.701            0.658         -0.720  \n",
       "Alan Mak               -0.666         -0.718           -0.694          0.925  \n",
       "\n",
       "[5 rows x 639 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarity(Non_Brexit_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Party Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "party_df = pd.read_csv(\"MPs_639.csv\")\n",
    "party_df = party_df.replace({'Id':{'Dr Th√©r√®se Coffey':'Dr Thérèse Coffey'}})\n",
    "party_df.set_index(\"Id\", inplace=True)\n",
    "\n",
    "#MemberParty = original\n",
    "#MemberParty9 = 9 parties; labour(coop) to labour; relabeled from 2 to 11 Change UK party members.\n",
    "#MemberParty8 = 8 parties; Change UK labeled to original party.  Independents left as is.\n",
    "#MemberParty8a = 8 parties; Change UK and Independents labeled to original party.\n",
    "#MemberParty3 = 3 parties: Left / Right / Ind; Change Party labeled L/R accdg to original party.\n",
    "#MemberParty2 = 2 parties: Left / Right.  Ind & Change labeled L/R accdg to original party.\n",
    "\n",
    "def party_similarity(col):\n",
    "    global party_sim\n",
    "    party= pd.get_dummies(col)\n",
    "    MPs1 = list(party.index.values)\n",
    "    party_sim = pd.DataFrame(np.dot(party,party.transpose()), columns=MPs1, index=MPs1)\n",
    "    party_sim = party_sim.astype(int).replace(0,-1)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <th>Adam Holloway</th>\n",
       "      <th>Afzal Khan</th>\n",
       "      <th>Alan Brown</th>\n",
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       "      <th>Alex Burghart</th>\n",
       "      <th>Alex Chalk</th>\n",
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       "      <th>Wayne David</th>\n",
       "      <th>Wendy Morton</th>\n",
       "      <th>Wera Hobhouse</th>\n",
       "      <th>Wes Streeting</th>\n",
       "      <th>Will Quince</th>\n",
       "      <th>Yasmin Qureshi</th>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <th>Yvonne Fovargue</th>\n",
       "      <th>Zac Goldsmith</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>-1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>1</td>\n",
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       "      <td>-1</td>\n",
       "      <td>1</td>\n",
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       "      <th>Afzal Khan</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Brown</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Mak</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albert Owen</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alberto Costa</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alec Shelbrooke</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Burghart</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>-1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Alex Chalk</th>\n",
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       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Cunningham</th>\n",
       "      <td>-1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Norris</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Sobel</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alison McGovern</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alison Thewliss</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alistair Burt</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alok Sharma</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alun Cairns</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amanda Milling</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amber Rudd</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea Jenkyns</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea Leadsom</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Bowie</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Bridgen</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Griffiths</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Gwynne</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Jones</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Lewer</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Percy</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Rosindell</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Theresa Villiers</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim Farron</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim Loughton</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toby Perkins</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Brake</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Pursglove</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Tugendhat</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Watson</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tommy Sheppard</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonia Antoniazzi</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tony Lloyd</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tracey Crouch</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tracy Brabin</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trudy Harrison</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tulip Siddiq</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valerie Vaz</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vernon Coaker</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vicky Ford</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vicky Foxcroft</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victoria Atkins</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victoria Prentis</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wayne David</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wendy Morton</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wera Hobhouse</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes Streeting</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Will Quince</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yasmin Qureshi</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yvonne Fovargue</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zac Goldsmith</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>639 rows × 639 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Adam Afriyie  Adam Holloway  Afzal Khan  Alan Brown  \\\n",
       "Adam Afriyie                 1              1          -1          -1   \n",
       "Adam Holloway                1              1          -1          -1   \n",
       "Afzal Khan                  -1             -1           1           1   \n",
       "Alan Brown                  -1             -1           1           1   \n",
       "Alan Mak                     1              1          -1          -1   \n",
       "Albert Owen                 -1             -1           1           1   \n",
       "Alberto Costa                1              1          -1          -1   \n",
       "Alec Shelbrooke              1              1          -1          -1   \n",
       "Alex Burghart                1              1          -1          -1   \n",
       "Alex Chalk                   1              1          -1          -1   \n",
       "Alex Cunningham             -1             -1           1           1   \n",
       "Alex Norris                 -1             -1           1           1   \n",
       "Alex Sobel                  -1             -1           1           1   \n",
       "Alison McGovern             -1             -1           1           1   \n",
       "Alison Thewliss             -1             -1           1           1   \n",
       "Alistair Burt                1              1          -1          -1   \n",
       "Alok Sharma                  1              1          -1          -1   \n",
       "Alun Cairns                  1              1          -1          -1   \n",
       "Amanda Milling               1              1          -1          -1   \n",
       "Amber Rudd                   1              1          -1          -1   \n",
       "Andrea Jenkyns               1              1          -1          -1   \n",
       "Andrea Leadsom               1              1          -1          -1   \n",
       "Andrew Bowie                 1              1          -1          -1   \n",
       "Andrew Bridgen               1              1          -1          -1   \n",
       "Andrew Griffiths             1              1          -1          -1   \n",
       "Andrew Gwynne               -1             -1           1           1   \n",
       "Andrew Jones                 1              1          -1          -1   \n",
       "Andrew Lewer                 1              1          -1          -1   \n",
       "Andrew Percy                 1              1          -1          -1   \n",
       "Andrew Rosindell             1              1          -1          -1   \n",
       "...                        ...            ...         ...         ...   \n",
       "Theresa Villiers             1              1          -1          -1   \n",
       "Tim Farron                  -1             -1           1           1   \n",
       "Tim Loughton                 1              1          -1          -1   \n",
       "Toby Perkins                -1             -1           1           1   \n",
       "Tom Brake                   -1             -1           1           1   \n",
       "Tom Pursglove                1              1          -1          -1   \n",
       "Tom Tugendhat                1              1          -1          -1   \n",
       "Tom Watson                  -1             -1           1           1   \n",
       "Tommy Sheppard              -1             -1           1           1   \n",
       "Tonia Antoniazzi            -1             -1           1           1   \n",
       "Tony Lloyd                  -1             -1           1           1   \n",
       "Tracey Crouch                1              1          -1          -1   \n",
       "Tracy Brabin                -1             -1           1           1   \n",
       "Trudy Harrison               1              1          -1          -1   \n",
       "Tulip Siddiq                -1             -1           1           1   \n",
       "Valerie Vaz                 -1             -1           1           1   \n",
       "Vernon Coaker               -1             -1           1           1   \n",
       "Vicky Ford                   1              1          -1          -1   \n",
       "Vicky Foxcroft              -1             -1           1           1   \n",
       "Victoria Atkins              1              1          -1          -1   \n",
       "Victoria Prentis             1              1          -1          -1   \n",
       "Wayne David                 -1             -1           1           1   \n",
       "Wendy Morton                 1              1          -1          -1   \n",
       "Wera Hobhouse               -1             -1           1           1   \n",
       "Wes Streeting               -1             -1           1           1   \n",
       "Will Quince                  1              1          -1          -1   \n",
       "Yasmin Qureshi              -1             -1           1           1   \n",
       "Yvette Cooper               -1             -1           1           1   \n",
       "Yvonne Fovargue             -1             -1           1           1   \n",
       "Zac Goldsmith                1              1          -1          -1   \n",
       "\n",
       "                  Alan Mak  Albert Owen  Alberto Costa  Alec Shelbrooke  \\\n",
       "Adam Afriyie             1           -1              1                1   \n",
       "Adam Holloway            1           -1              1                1   \n",
       "Afzal Khan              -1            1             -1               -1   \n",
       "Alan Brown              -1            1             -1               -1   \n",
       "Alan Mak                 1           -1              1                1   \n",
       "Albert Owen             -1            1             -1               -1   \n",
       "Alberto Costa            1           -1              1                1   \n",
       "Alec Shelbrooke          1           -1              1                1   \n",
       "Alex Burghart            1           -1              1                1   \n",
       "Alex Chalk               1           -1              1                1   \n",
       "Alex Cunningham         -1            1             -1               -1   \n",
       "Alex Norris             -1            1             -1               -1   \n",
       "Alex Sobel              -1            1             -1               -1   \n",
       "Alison McGovern         -1            1             -1               -1   \n",
       "Alison Thewliss         -1            1             -1               -1   \n",
       "Alistair Burt            1           -1              1                1   \n",
       "Alok Sharma              1           -1              1                1   \n",
       "Alun Cairns              1           -1              1                1   \n",
       "Amanda Milling           1           -1              1                1   \n",
       "Amber Rudd               1           -1              1                1   \n",
       "Andrea Jenkyns           1           -1              1                1   \n",
       "Andrea Leadsom           1           -1              1                1   \n",
       "Andrew Bowie             1           -1              1                1   \n",
       "Andrew Bridgen           1           -1              1                1   \n",
       "Andrew Griffiths         1           -1              1                1   \n",
       "Andrew Gwynne           -1            1             -1               -1   \n",
       "Andrew Jones             1           -1              1                1   \n",
       "Andrew Lewer             1           -1              1                1   \n",
       "Andrew Percy             1           -1              1                1   \n",
       "Andrew Rosindell         1           -1              1                1   \n",
       "...                    ...          ...            ...              ...   \n",
       "Theresa Villiers         1           -1              1                1   \n",
       "Tim Farron              -1            1             -1               -1   \n",
       "Tim Loughton             1           -1              1                1   \n",
       "Toby Perkins            -1            1             -1               -1   \n",
       "Tom Brake               -1            1             -1               -1   \n",
       "Tom Pursglove            1           -1              1                1   \n",
       "Tom Tugendhat            1           -1              1                1   \n",
       "Tom Watson              -1            1             -1               -1   \n",
       "Tommy Sheppard          -1            1             -1               -1   \n",
       "Tonia Antoniazzi        -1            1             -1               -1   \n",
       "Tony Lloyd              -1            1             -1               -1   \n",
       "Tracey Crouch            1           -1              1                1   \n",
       "Tracy Brabin            -1            1             -1               -1   \n",
       "Trudy Harrison           1           -1              1                1   \n",
       "Tulip Siddiq            -1            1             -1               -1   \n",
       "Valerie Vaz             -1            1             -1               -1   \n",
       "Vernon Coaker           -1            1             -1               -1   \n",
       "Vicky Ford               1           -1              1                1   \n",
       "Vicky Foxcroft          -1            1             -1               -1   \n",
       "Victoria Atkins          1           -1              1                1   \n",
       "Victoria Prentis         1           -1              1                1   \n",
       "Wayne David             -1            1             -1               -1   \n",
       "Wendy Morton             1           -1              1                1   \n",
       "Wera Hobhouse           -1            1             -1               -1   \n",
       "Wes Streeting           -1            1             -1               -1   \n",
       "Will Quince              1           -1              1                1   \n",
       "Yasmin Qureshi          -1            1             -1               -1   \n",
       "Yvette Cooper           -1            1             -1               -1   \n",
       "Yvonne Fovargue         -1            1             -1               -1   \n",
       "Zac Goldsmith            1           -1              1                1   \n",
       "\n",
       "                  Alex Burghart  Alex Chalk      ...        Victoria Prentis  \\\n",
       "Adam Afriyie                  1           1      ...                       1   \n",
       "Adam Holloway                 1           1      ...                       1   \n",
       "Afzal Khan                   -1          -1      ...                      -1   \n",
       "Alan Brown                   -1          -1      ...                      -1   \n",
       "Alan Mak                      1           1      ...                       1   \n",
       "Albert Owen                  -1          -1      ...                      -1   \n",
       "Alberto Costa                 1           1      ...                       1   \n",
       "Alec Shelbrooke               1           1      ...                       1   \n",
       "Alex Burghart                 1           1      ...                       1   \n",
       "Alex Chalk                    1           1      ...                       1   \n",
       "Alex Cunningham              -1          -1      ...                      -1   \n",
       "Alex Norris                  -1          -1      ...                      -1   \n",
       "Alex Sobel                   -1          -1      ...                      -1   \n",
       "Alison McGovern              -1          -1      ...                      -1   \n",
       "Alison Thewliss              -1          -1      ...                      -1   \n",
       "Alistair Burt                 1           1      ...                       1   \n",
       "Alok Sharma                   1           1      ...                       1   \n",
       "Alun Cairns                   1           1      ...                       1   \n",
       "Amanda Milling                1           1      ...                       1   \n",
       "Amber Rudd                    1           1      ...                       1   \n",
       "Andrea Jenkyns                1           1      ...                       1   \n",
       "Andrea Leadsom                1           1      ...                       1   \n",
       "Andrew Bowie                  1           1      ...                       1   \n",
       "Andrew Bridgen                1           1      ...                       1   \n",
       "Andrew Griffiths              1           1      ...                       1   \n",
       "Andrew Gwynne                -1          -1      ...                      -1   \n",
       "Andrew Jones                  1           1      ...                       1   \n",
       "Andrew Lewer                  1           1      ...                       1   \n",
       "Andrew Percy                  1           1      ...                       1   \n",
       "Andrew Rosindell              1           1      ...                       1   \n",
       "...                         ...         ...      ...                     ...   \n",
       "Theresa Villiers              1           1      ...                       1   \n",
       "Tim Farron                   -1          -1      ...                      -1   \n",
       "Tim Loughton                  1           1      ...                       1   \n",
       "Toby Perkins                 -1          -1      ...                      -1   \n",
       "Tom Brake                    -1          -1      ...                      -1   \n",
       "Tom Pursglove                 1           1      ...                       1   \n",
       "Tom Tugendhat                 1           1      ...                       1   \n",
       "Tom Watson                   -1          -1      ...                      -1   \n",
       "Tommy Sheppard               -1          -1      ...                      -1   \n",
       "Tonia Antoniazzi             -1          -1      ...                      -1   \n",
       "Tony Lloyd                   -1          -1      ...                      -1   \n",
       "Tracey Crouch                 1           1      ...                       1   \n",
       "Tracy Brabin                 -1          -1      ...                      -1   \n",
       "Trudy Harrison                1           1      ...                       1   \n",
       "Tulip Siddiq                 -1          -1      ...                      -1   \n",
       "Valerie Vaz                  -1          -1      ...                      -1   \n",
       "Vernon Coaker                -1          -1      ...                      -1   \n",
       "Vicky Ford                    1           1      ...                       1   \n",
       "Vicky Foxcroft               -1          -1      ...                      -1   \n",
       "Victoria Atkins               1           1      ...                       1   \n",
       "Victoria Prentis              1           1      ...                       1   \n",
       "Wayne David                  -1          -1      ...                      -1   \n",
       "Wendy Morton                  1           1      ...                       1   \n",
       "Wera Hobhouse                -1          -1      ...                      -1   \n",
       "Wes Streeting                -1          -1      ...                      -1   \n",
       "Will Quince                   1           1      ...                       1   \n",
       "Yasmin Qureshi               -1          -1      ...                      -1   \n",
       "Yvette Cooper                -1          -1      ...                      -1   \n",
       "Yvonne Fovargue              -1          -1      ...                      -1   \n",
       "Zac Goldsmith                 1           1      ...                       1   \n",
       "\n",
       "                  Wayne David  Wendy Morton  Wera Hobhouse  Wes Streeting  \\\n",
       "Adam Afriyie               -1             1             -1             -1   \n",
       "Adam Holloway              -1             1             -1             -1   \n",
       "Afzal Khan                  1            -1              1              1   \n",
       "Alan Brown                  1            -1              1              1   \n",
       "Alan Mak                   -1             1             -1             -1   \n",
       "Albert Owen                 1            -1              1              1   \n",
       "Alberto Costa              -1             1             -1             -1   \n",
       "Alec Shelbrooke            -1             1             -1             -1   \n",
       "Alex Burghart              -1             1             -1             -1   \n",
       "Alex Chalk                 -1             1             -1             -1   \n",
       "Alex Cunningham             1            -1              1              1   \n",
       "Alex Norris                 1            -1              1              1   \n",
       "Alex Sobel                  1            -1              1              1   \n",
       "Alison McGovern             1            -1              1              1   \n",
       "Alison Thewliss             1            -1              1              1   \n",
       "Alistair Burt              -1             1             -1             -1   \n",
       "Alok Sharma                -1             1             -1             -1   \n",
       "Alun Cairns                -1             1             -1             -1   \n",
       "Amanda Milling             -1             1             -1             -1   \n",
       "Amber Rudd                 -1             1             -1             -1   \n",
       "Andrea Jenkyns             -1             1             -1             -1   \n",
       "Andrea Leadsom             -1             1             -1             -1   \n",
       "Andrew Bowie               -1             1             -1             -1   \n",
       "Andrew Bridgen             -1             1             -1             -1   \n",
       "Andrew Griffiths           -1             1             -1             -1   \n",
       "Andrew Gwynne               1            -1              1              1   \n",
       "Andrew Jones               -1             1             -1             -1   \n",
       "Andrew Lewer               -1             1             -1             -1   \n",
       "Andrew Percy               -1             1             -1             -1   \n",
       "Andrew Rosindell           -1             1             -1             -1   \n",
       "...                       ...           ...            ...            ...   \n",
       "Theresa Villiers           -1             1             -1             -1   \n",
       "Tim Farron                  1            -1              1              1   \n",
       "Tim Loughton               -1             1             -1             -1   \n",
       "Toby Perkins                1            -1              1              1   \n",
       "Tom Brake                   1            -1              1              1   \n",
       "Tom Pursglove              -1             1             -1             -1   \n",
       "Tom Tugendhat              -1             1             -1             -1   \n",
       "Tom Watson                  1            -1              1              1   \n",
       "Tommy Sheppard              1            -1              1              1   \n",
       "Tonia Antoniazzi            1            -1              1              1   \n",
       "Tony Lloyd                  1            -1              1              1   \n",
       "Tracey Crouch              -1             1             -1             -1   \n",
       "Tracy Brabin                1            -1              1              1   \n",
       "Trudy Harrison             -1             1             -1             -1   \n",
       "Tulip Siddiq                1            -1              1              1   \n",
       "Valerie Vaz                 1            -1              1              1   \n",
       "Vernon Coaker               1            -1              1              1   \n",
       "Vicky Ford                 -1             1             -1             -1   \n",
       "Vicky Foxcroft              1            -1              1              1   \n",
       "Victoria Atkins            -1             1             -1             -1   \n",
       "Victoria Prentis           -1             1             -1             -1   \n",
       "Wayne David                 1            -1              1              1   \n",
       "Wendy Morton               -1             1             -1             -1   \n",
       "Wera Hobhouse               1            -1              1              1   \n",
       "Wes Streeting               1            -1              1              1   \n",
       "Will Quince                -1             1             -1             -1   \n",
       "Yasmin Qureshi              1            -1              1              1   \n",
       "Yvette Cooper               1            -1              1              1   \n",
       "Yvonne Fovargue             1            -1              1              1   \n",
       "Zac Goldsmith              -1             1             -1             -1   \n",
       "\n",
       "                  Will Quince  Yasmin Qureshi  Yvette Cooper  Yvonne Fovargue  \\\n",
       "Adam Afriyie                1              -1             -1               -1   \n",
       "Adam Holloway               1              -1             -1               -1   \n",
       "Afzal Khan                 -1               1              1                1   \n",
       "Alan Brown                 -1               1              1                1   \n",
       "Alan Mak                    1              -1             -1               -1   \n",
       "Albert Owen                -1               1              1                1   \n",
       "Alberto Costa               1              -1             -1               -1   \n",
       "Alec Shelbrooke             1              -1             -1               -1   \n",
       "Alex Burghart               1              -1             -1               -1   \n",
       "Alex Chalk                  1              -1             -1               -1   \n",
       "Alex Cunningham            -1               1              1                1   \n",
       "Alex Norris                -1               1              1                1   \n",
       "Alex Sobel                 -1               1              1                1   \n",
       "Alison McGovern            -1               1              1                1   \n",
       "Alison Thewliss            -1               1              1                1   \n",
       "Alistair Burt               1              -1             -1               -1   \n",
       "Alok Sharma                 1              -1             -1               -1   \n",
       "Alun Cairns                 1              -1             -1               -1   \n",
       "Amanda Milling              1              -1             -1               -1   \n",
       "Amber Rudd                  1              -1             -1               -1   \n",
       "Andrea Jenkyns              1              -1             -1               -1   \n",
       "Andrea Leadsom              1              -1             -1               -1   \n",
       "Andrew Bowie                1              -1             -1               -1   \n",
       "Andrew Bridgen              1              -1             -1               -1   \n",
       "Andrew Griffiths            1              -1             -1               -1   \n",
       "Andrew Gwynne              -1               1              1                1   \n",
       "Andrew Jones                1              -1             -1               -1   \n",
       "Andrew Lewer                1              -1             -1               -1   \n",
       "Andrew Percy                1              -1             -1               -1   \n",
       "Andrew Rosindell            1              -1             -1               -1   \n",
       "...                       ...             ...            ...              ...   \n",
       "Theresa Villiers            1              -1             -1               -1   \n",
       "Tim Farron                 -1               1              1                1   \n",
       "Tim Loughton                1              -1             -1               -1   \n",
       "Toby Perkins               -1               1              1                1   \n",
       "Tom Brake                  -1               1              1                1   \n",
       "Tom Pursglove               1              -1             -1               -1   \n",
       "Tom Tugendhat               1              -1             -1               -1   \n",
       "Tom Watson                 -1               1              1                1   \n",
       "Tommy Sheppard             -1               1              1                1   \n",
       "Tonia Antoniazzi           -1               1              1                1   \n",
       "Tony Lloyd                 -1               1              1                1   \n",
       "Tracey Crouch               1              -1             -1               -1   \n",
       "Tracy Brabin               -1               1              1                1   \n",
       "Trudy Harrison              1              -1             -1               -1   \n",
       "Tulip Siddiq               -1               1              1                1   \n",
       "Valerie Vaz                -1               1              1                1   \n",
       "Vernon Coaker              -1               1              1                1   \n",
       "Vicky Ford                  1              -1             -1               -1   \n",
       "Vicky Foxcroft             -1               1              1                1   \n",
       "Victoria Atkins             1              -1             -1               -1   \n",
       "Victoria Prentis            1              -1             -1               -1   \n",
       "Wayne David                -1               1              1                1   \n",
       "Wendy Morton                1              -1             -1               -1   \n",
       "Wera Hobhouse              -1               1              1                1   \n",
       "Wes Streeting              -1               1              1                1   \n",
       "Will Quince                 1              -1             -1               -1   \n",
       "Yasmin Qureshi             -1               1              1                1   \n",
       "Yvette Cooper              -1               1              1                1   \n",
       "Yvonne Fovargue            -1               1              1                1   \n",
       "Zac Goldsmith               1              -1             -1               -1   \n",
       "\n",
       "                  Zac Goldsmith  \n",
       "Adam Afriyie                  1  \n",
       "Adam Holloway                 1  \n",
       "Afzal Khan                   -1  \n",
       "Alan Brown                   -1  \n",
       "Alan Mak                      1  \n",
       "Albert Owen                  -1  \n",
       "Alberto Costa                 1  \n",
       "Alec Shelbrooke               1  \n",
       "Alex Burghart                 1  \n",
       "Alex Chalk                    1  \n",
       "Alex Cunningham              -1  \n",
       "Alex Norris                  -1  \n",
       "Alex Sobel                   -1  \n",
       "Alison McGovern              -1  \n",
       "Alison Thewliss              -1  \n",
       "Alistair Burt                 1  \n",
       "Alok Sharma                   1  \n",
       "Alun Cairns                   1  \n",
       "Amanda Milling                1  \n",
       "Amber Rudd                    1  \n",
       "Andrea Jenkyns                1  \n",
       "Andrea Leadsom                1  \n",
       "Andrew Bowie                  1  \n",
       "Andrew Bridgen                1  \n",
       "Andrew Griffiths              1  \n",
       "Andrew Gwynne                -1  \n",
       "Andrew Jones                  1  \n",
       "Andrew Lewer                  1  \n",
       "Andrew Percy                  1  \n",
       "Andrew Rosindell              1  \n",
       "...                         ...  \n",
       "Theresa Villiers              1  \n",
       "Tim Farron                   -1  \n",
       "Tim Loughton                  1  \n",
       "Toby Perkins                 -1  \n",
       "Tom Brake                    -1  \n",
       "Tom Pursglove                 1  \n",
       "Tom Tugendhat                 1  \n",
       "Tom Watson                   -1  \n",
       "Tommy Sheppard               -1  \n",
       "Tonia Antoniazzi             -1  \n",
       "Tony Lloyd                   -1  \n",
       "Tracey Crouch                 1  \n",
       "Tracy Brabin                 -1  \n",
       "Trudy Harrison                1  \n",
       "Tulip Siddiq                 -1  \n",
       "Valerie Vaz                  -1  \n",
       "Vernon Coaker                -1  \n",
       "Vicky Ford                    1  \n",
       "Vicky Foxcroft               -1  \n",
       "Victoria Atkins               1  \n",
       "Victoria Prentis              1  \n",
       "Wayne David                  -1  \n",
       "Wendy Morton                  1  \n",
       "Wera Hobhouse                -1  \n",
       "Wes Streeting                -1  \n",
       "Will Quince                   1  \n",
       "Yasmin Qureshi               -1  \n",
       "Yvette Cooper                -1  \n",
       "Yvonne Fovargue              -1  \n",
       "Zac Goldsmith                 1  \n",
       "\n",
       "[639 rows x 639 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party= pd.get_dummies(party_df['MemberParty2'])\n",
    "MPs1 = list(party.index.values)\n",
    "party_sim = pd.DataFrame(np.dot(party,party.transpose()), columns=MPs1, index=MPs1)\n",
    "party_sim = party_sim.astype(int).replace(0,-1)\n",
    "party_sim.sort_index(axis=1, inplace=True)\n",
    "party_sim.sort_index(axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Party difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "partylist = ['9','8','8a','3','2']\n",
    "\n",
    "def party_difference(table):    \n",
    "    for item in partylist:\n",
    "        party_similarity(party_df['MemberParty'+item])\n",
    "        ps = pd.DataFrame(1-pairwise_distances(table, metric =\"cosine\"), columns=MPs, index=MPs).round(3) - party_sim\n",
    "        iu1 = np.triu_indices(639) \n",
    "        triangle_upper = ps.values[iu1]\n",
    "        plt.figure(figsize=(8, 20))\n",
    "        plt.subplot(5,1,partylist.index(item)+1)\n",
    "        plt.hist(triangle_upper, range=[-2, 2], bins=\"auto\")\n",
    "        plt.title(item+' parties')\n",
    "        #ps.round(3).to_csv(\"ps.csv\")\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_difference(overall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ks8+l5cErsUmS1EEGuCRJHWSAS5LUQQa4JEkdZIBLktRBBrgkSR1kgEuS1EEGuCRJHWSAS1pyXnxGGp4BLklSBxngkiR1kAEujRGHoqXlwwCXJKmDDHBJkjpo3gBPsibJF5NsT3JHkje18iOSbEuyo92vauVJckmSnUluS3JS37o2tvo7kmw8dN2Slh+Hz6XlZZA98IeB/1hVzwdOBS5IcjywGbi+qtYB17d5gDOBde22CbgUeoEPXAicQu//iF84HfqS5BcQaTjzBnhV3VtVX2nTPwK2A6uBDcCWVm0LcHab3gBcWT03AiuTHAOcAWyrqn1VdT+wDVi/oL2RJOkAde1L5FDHwJOsBV4I3AQcXVX3Qi/kgaNatdXAPX0P29XKZiuXJGlJdS28YYgAT/IU4G+BN1fVg3NVnaGs5ijf/3k2JZlKMrV3795BmydJ0rIyUIAneTy98P5wVX2iFd/XhsZp93ta+S5gTd/DjwV2z1H+KFV1WVVNVtXkxMTEMH2RJGnZGOQs9ACXA9ur6i/6Fm0Fps8k3whc01d+Xjsb/VTggTbEfh1wepJV7eS101uZpBHRxWFEablaMUCdlwC/A9ye5NZW9p+Ai4GrkpwPfBc4py27FjgL2An8FHgdQFXtS/I24OZW76Kq2rcgvZAkaZmZN8Cr6n8x8/FrgNNmqF/ABbOs6wrgimEaKGn0uecuLT6vxCZJUgcZ4JIkdZABLklS06XDQQa4JEkdZIBLktRBBrg0Bro07CdpYQzyO3BJksZSl7/8ugcuaWR0+Y+ptNgMcEmSOsgAlySpgwxwSZI6yACXNFI8Di4NxgCXOs7Ak5YnA1yStCx1/cuvAS5JUgfNG+BJrkiyJ8nX+8qOSLItyY52v6qVJ8klSXYmuS3JSX2P2djq70iy8dB0R5Kkg9OVPfNB9sA/AKzfr2wzcH1VrQOub/MAZwLr2m0TcCn0Ah+4EDgFOBm4cDr0JR24rvyhGdbazZ/55U3SzOYN8Kr6ErBvv+INwJY2vQU4u6/8yuq5EViZ5BjgDGBbVe2rqvuBbTz2S4EkSRrQgR4DP7qq7gVo90e18tXAPX31drWy2cofI8mmJFNJpvbu3XuAzZMkaXbjMLqz0CexZYaymqP8sYVVl1XVZFVNTkxMLGjjJEkaFwf638juS3JMVd3bhsj3tPJdwJq+escCu1v5S/crv+EAn1ta1sZhz2EYazd/hrsvfuVSN0MaOQe6B74VmD6TfCNwTV/5ee1s9FOBB9oQ+3XA6UlWtZPXTm9lkiTpAAzyM7KPAP8HeF6SXUnOBy4GXpFkB/CKNg9wLXAXsBP4a+ANAFW1D3gbcHO7XdTKJHXcYowITD/Hcht90KExLu+jeYfQq+q1syw6bYa6BVwwy3quAK4YqnWS1IzLH11poRzoMXBJS8AQe/Rr4LFxLWdeSlXqCMNbWjxd+LwZ4JJm1IU/YF1oo3SoGODSiPOSonPztdEwxun9YoBL6rxx+qMsDcoAl5ZYf/gYRJIGZYBLGguDfvnxS9LyNW7b3p+RSUtopguUdOmPzCi2dbZLr870GvszNHWZe+DSEhnF8INunzTX/4VokD50tZ8SGOCSxliXv4xoYY3j+8AhdGmRjOMfkK6Y67X3v52Nt3H+3LkHLi2Ccf4jIo2rUf/cGuCSlr1hj52rGxZiW47ye8IhdGkB9Z/dPKofes3M7aWuSe8/gI6mycnJmpqaWupmSPMa1z/+cx0bHtc+78/j4910KN6fi/VeSHJLVU3OV2/R98CTrAfeAxwGvL+qLl7sNkgLabkE2XI122/G5yvv55eAhTHXZ2161OtQjn6N2gmPi7oHnuQw4P8CrwB2ATcDr62qO2eq7x74gRm1N9mhsP8fz4Xos0H8WPNdEEXDW6j36VJ9xg90+w/S3v0/x6P4XluM133QPfDFDvAXAX9SVWe0+bcAVNU7ZqpvgM/vYN/gw7wZhzm+O+x6PWY8mgbZu9Ro6v9yq4V1qEN8VAP8NcD6qvr9Nv87wClV9ca+OpuATW32ecA3F7gZRwLfX+B1LoVx6QfYl1E1Ln0Zl36AfRlFh6Ifz66qifkqLfYx8MxQ9qhvEFV1GXDZIWtAMjXIN5tRNy79APsyqsalL+PSD7Avo2gp+7HYvwPfBazpmz8W2L3IbZAkqfMWO8BvBtYlOS7JE4Bzga2L3AZJkjpvUYfQq+rhJG8ErqP3M7IrquqOxWwDh3B4fpGNSz/AvoyqcenLuPQD7MsoWrJ+jPSFXCRJ0sy8FrokSR1kgEuS1EFjH+BJ/jzJN5LcluSTSVbOUm99km8m2Zlk82K3cz5JzklyR5JfJJn1JwtJ7k5ye5Jbk4zkVXCG6MtIbxOAJEck2ZZkR7tfNUu9R9o2uTXJyJy4Od9rnOSJST7Wlt+UZO3it3IwA/Tld5Ps7dsOv78U7ZxPkiuS7Eny9VmWJ8klrZ+3JTlpsds4qAH68tIkD/Rtk/+y2G0cRJI1Sb6YZHv72/WmGeos/napqrG+AacDK9r0O4F3zlDnMOBbwHOAJwBfA45f6rbv18bn07uwzQ3A5Bz17gaOXOr2HmxfurBNWjvfBWxu05tnen+1ZT9e6rYeyGsMvAH4qzZ9LvCxpW73QfTld4H3LnVbB+jLbwAnAV+fZflZwGfpXVfjVOCmpW7zQfTlpcCnl7qdA/TjGOCkNv1UepcE3//9tejbZez3wKvq81X1cJu9kd5vz/d3MrCzqu6qqn8APgpsWKw2DqKqtlfVQl+VbkkM2JeR3ybNBmBLm94CnL2EbRnWIK9xf/+uBk5LMtMFmZZaV94v86qqLwH75qiyAbiyem4EViY5ZnFaN5wB+tIJVXVvVX2lTf8I2A6s3q/aom+XsQ/w/fwevW9I+1sN3NM3v4vHbpyuKODzSW5pl6Xtqq5sk6Or6l7ofciBo2apd3iSqSQ3JhmVkB/kNf5lnfZF+AHg6YvSuuEM+n75V2148+oka2ZY3gVd+WwM6kVJvpbks0lOWOrGzKcdRnohcNN+ixZ9uyz6vxM9FJL8D+AZMyx6a1Vd0+q8FXgY+PBMq5ihbNF/XzdIPwbwkqraneQoYFuSb7RvwYtqAfoyEtsE5u7LEKt5VtsuzwG+kOT2qvrWwrTwgA3yGo/MdpjHIO38O+AjVfVQktfTG1l42SFv2cLryjYZxFfoXff7x0nOAj4FrFviNs0qyVOAvwXeXFUP7r94hocc0u0yFgFeVS+fa3mSjcCrgNOqHazYz0hc4nW+fgy4jt3tfk+ST9IbWlz0AF+AvozENoG5+5LkviTHVNW9bbhszyzrmN4udyW5gd43+KUO8EFe4+k6u5KsAJ7GaA6JztuXqvpB3+xf0zsnpotG5rNxsPpDsKquTfK+JEdW1cj9k5Mkj6cX3h+uqk/MUGXRt8vYD6EnWQ/8MfDqqvrpLNXG4hKvSZ6c5KnT0/RO4Jvx7M8O6Mo22QpsbNMbgceMLiRZleSJbfpI4CXAnYvWwtkN8hr39+81wBdm+RK81Obty37HI19N7zhmF20FzmtnPZ8KPDB9GKdrkjxj+pyKJCfTy6QfzP2oxdfaeDmwvar+YpZqi79dlvrsvkN9A3bSOy5xa7tNn1H7TODavnpn0Tuz8Fv0hnmXvO379eNf0PuG9xBwH3Dd/v2gdwbu19rtjlHsx6B96cI2aW18OnA9sKPdH9HKJ4H3t+kXA7e37XI7cP5St3uu1xi4iN4XXoDDgY+3z9GXgecsdZsPoi/vaJ+LrwFfBH59qds8Sz8+AtwL/Lx9Ts4HXg+8vi0P8Jetn7czx69Slvo2QF/e2LdNbgRevNRtnqUf/5TecPhtfVly1lJvFy+lKklSB439ELokSePIAJckqYMMcEmSOsgAlySpgwxwSZI6yACXJKmDDHBJkjro/wOZGdeRvEfycAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_difference(Brexit_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cA+yqqgNV9SCwCzh3se2SJGmSrVmKjSTZALwAuBk4sar2wSD8k5zQqq0D7h1abaqVzVY+09/ZxuBsn2c84xlL0XSpa37sS5o8I188l+SngU8Av1JVfzlX1RnKao7yxxZWXV5Vm6pq09q1aw+/sZIkdW6kYE/yBAah/pGq+mQrvr8NsdPuH2jlU8ApQ6uvB+6bo1ySJB2mUa6KD3AFsKeqfndo0Q7g0JXtW4Frh8pf266OPxN4uA3ZXw+cneSYdtHc2a1MkhbN32bXpBrlPfYXA68Bbk/ypVb2duDdwNVJLga+CVzYll0HnA/sBb4PvB6gqg4keSdwS6v3jqo6MEK7JEmaWIsO9qr6X8z8/jjA5hnqF3DJLNu6ErhysW2R9FierUqTaUmuipekxfIARFpafqWsJEkd8Yxd6oxnwNJk84xdkqSOGOySuuYIxmQ59DHHSd7vBrukeU3yi6TGw6SH+TCDXZLUpUkNeoNdkjTW5grwSQx3g12SpI4Y7FJHJvHsRNKjGeySuucBjyaJX1AjSRpLHrDNzDN2SZJmMK4HDga7pIng55wn1+Hs+x6eIw7FSx0Y1xejcW23Vs5yPGfG/XnpGbs05sb9RUhaLvN93r2X/yXP2CUdlg3bd/KNd79ipZuxaOPefo2ml/Cei2fskqSxsBKhPI5n8p6xS5JWtXEL1pVmsEtjyBe60Rx6/ByS10KN01s4DsVLY2A4yHsI9dUyvLka2qC5raZ9tJraMhfP2KUxMS4vKuPGs/fVy+f84hjs0irli9ryGn68DfmV5XN/NAa7JE1jyGs24zDCs2qCPcm5wO8DRwAfqKp3r3CTpGU3CWcq49bHcbpoqgfj9vxYjVZFsCc5AvgD4OXAFHBLkh1V9dWVbZm0eNNfoOYKB1/MNGkHEOP+nF/N+ytVtdJtIMkLgd+oqnPa/NsAquq3Zltn06ZNtXv37mVqoTS7cX+BWozFvKD1+DiN+sI+12OyWkJjofttent73N+zWa59leTWqto0b71VEuyvBs6tqje0+dcAZ1TVm6bV2wZsa7M/B9y1hM04Hvj2Em5vJfXSl176AfZlteqlL730A+zLXJ5ZVWvnq7QqhuKBzFD2mCOOqrocuPxxaUCyeyFHQuOgl7700g+wL6tVL33ppR9gX5bCavmCminglKH59cB9K9QWSZLG1moJ9luAjUlOTXIkcBGwY4XbJEnS2FkVQ/FVdTDJm4DrGXzc7cqqumOZm/G4DPGvkF760ks/wL6sVr30pZd+gH0Z2aq4eE6SJC2N1TIUL0mSloDBLklSRyY22JP8hyR3JrktyR8nOXqWeucmuSvJ3iTbl7udC5HkwiR3JPlxklk/WpHkG0luT/KlJKvu230Oox/jsE+OTbIryd3t/phZ6v2o7Y8vJVlVF4zO9zgnOSrJx9vym5NsWP5Wzm8B/Xhdkv1D++ENK9HO+SS5MskDSb4yy/Ikuaz187Ykpy13GxdqAX05K8nDQ/vk3y13GxciySlJPptkT3vtessMdZZ/v1TVRN6As4E1bfo9wHtmqHME8DXgWcCRwJeB565022do53MYfGHP54BNc9T7BnD8Srd3lH6M0T75bWB7m94+0/OrLfveSrd1sY8z8MvAf27TFwEfX+l2L7IfrwP+00q3dQF9eSlwGvCVWZafD3yawfeCnAncvNJtHqEvZwGfWul2LqAfJwGntemfAf7vDM+vZd8vE3vGXlWfqaqDbfYmBp+dn+50YG9V3VNVfw18DNiyXG1cqKraU1VL+S18K2KB/RiLfcKgTVe16auAC1awLYuxkMd5uI/XAJuTzPRlUytpXJ4v86qqPwUOzFFlC/DBGrgJODrJScvTusOzgL6MharaV1VfaNPfBfYA66ZVW/b9MrHBPs0/Z3BENd064N6h+Skeu9PGSQGfSXJr+3recTQu++TEqtoHg39+4IRZ6j0xye4kNyVZTeG/kMf5J3XaQfLDwHHL0rqFW+jz5R+1YdJrkpwyw/JxMC7/Gwv1wiRfTvLpJM9b6cbMp70V9QLg5mmLln2/rIrPsT9ekvwP4OkzLPr1qrq21fl14CDwkZk2MUPZinw+cCF9WYAXV9V9SU4AdiW5sx05L5sl6MdY7JPD2Mwz2j55FnBjktur6mtL08KRLORxXjX7Yg4LaeN/Bz5aVY8keSODUYiXPe4tW3rjsD8W6gsMvhf9e0nOB/4bsHGF2zSrJD8NfAL4lar6y+mLZ1jlcd0vXQd7Vf3CXMuTbAVeCWyu9mbINKvmq27n68sCt3Ffu38gyR8zGKZc1mBfgn6MxT5Jcn+Sk6pqXxt2e2CWbRzaJ/ck+RyDI/7VEOwLeZwP1ZlKsgZ4GqtveHXeflTVd4Zm/5DBNTfjaNX8b4xqOByr6rok70tyfFWtuh+HSfIEBqH+kar65AxVln2/TOxQfJJzgbcCr6qq789SrZuvuk3ylCQ/c2iawcWDM16RusqNyz7ZAWxt01uBx4xGJDkmyVFt+njgxcBXl62Fc1vI4zzcx1cDN85ygLyS5u3HtPc7X8XgfdJxtAN4bbsK+0zg4UNvB42bJE8/dL1GktMZZNV35l5r+bU2XgHsqarfnaXa8u+Xlb6qcKVuwF4G73t8qd0OXd17MnDdUL3zGVzp+DUGw8Ur3vYZ+vKLDI4KHwHuB66f3hcGVwV/ud3uWI19WUg/xmifHAfcANzd7o9t5ZuAD7TpFwG3t31yO3DxSrd7Wh8e8zgD72BwMAzwROC/tv+lzwPPWuk2L7Ifv9X+J74MfBZ49kq3eZZ+fBTYB/yw/Z9cDLwReGNbHuAPWj9vZ45PyKz0bQF9edPQPrkJeNFKt3mWfryEwbD6bUNZcv5K7xe/UlaSpI5M7FC8JEk9MtglSeqIwS5JUkcMdkmSOmKwS5LUEYNdkqSOGOySJHXk/wMTk6Wbh61W1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nAHuq6kBVHQT2AFtX2i5JkqbZqnzGnmQGeCZwE/CEqrofeuEPnNSqbQLu7VttXytbqHzQ79mRZC7J3P79+1ej6ZIkdcrQwZ7k0cAHgd+qqm8vVnVAWS1SfmRh1SVVNVtVsxs3bjz6xkqS1HFDBXuSh9EL9Suq6kOt+OttiJ3284FWvg/Y3Lf6KcB9i5RLGsK035xm2vuv6TXMVfEBLgXurKo/7lu0C5i/sn07cHVf+YXt6vgzgQfbUP21wNlJNrSL5s5uZZIk6SgNc0vZ5wAvBW5L8tlW9rvAm4Erk1wE3AOc35ZdA5wL7AW+B7wcoKoOJHkjcHOr94aqOjBEuyRJmlorDvaq+msGfz4OcNaA+gW8eoFtXQZcttK2SJKkHu88J3WQny9L08v/7iZppDwIkVaXZ+ySJHWIwS5JUocY7JI6a2bnbof6NXUMdklLMhw17voP4qb9gM5glySpQ7wqXuqQaT5LkcC/AfCMXdIU8M1+Ok3rfjfYJUkTbVoDfCEGu9QRvrlJAoNdkjTBljqgncYr5A12SZI6xKviJUkTZ9rOwo+GwS5NON/glmf+ebr7zS8YcUukY8uheEnSRPFgdnEGu6SR8Q1aa2WaXmsOxUsTaJRvUjM7d0/0cPakt3+aTVM4D8MzdkmSBpjUAwnP2CVNHS+kmzyrEbL92zh83/e/JiY10OcZ7NIEmPQ3mnHlsPz4O1av/YW2e3j5JB4EOhQvaap50KTlmKTXiWfs0pibpDeU5Rq3Pk3iWdk0GLfXyaQw2KUx5Zva2lvsM1itnXF97U/KAaBD8dIYGtc3tmniPhiNSXjex72NY3PGnmQr8DbgOODdVfXmETdJWnPj/oaxGiapj5NyhtYVk/TaGGdjEexJjgP+BPinwD7g5iS7quqO0bZMWplBb1BLhYNvatNrmg8gJvV1P87fqEhVjboNJHkW8PqqOqfNvw6gqv5woXVmZ2drbm5ujVooLWxS35iGsZI3tK49T8O+qS/n+RhlcMwH19Hst/n2duk74cuxVvspyS1VNbtkvTEJ9hcDW6vqN9v8S4Ezquo1h9XbAexos08BvriKzTgR+MYqbm+UutKXrvQD7Mu46kpfutIPsC+L+cWq2rhUpbEYigcyoOyII46qugS45Jg0IJlbzpHQJOhKX7rSD7Av46orfelKP8C+rIZxuSp+H7C5b/4U4L4RtUWSpIk1LsF+M7AlyalJjgcuAHaNuE2SJE2csRiKr6pDSV4DXEvv626XVdXta9yMYzLEPyJd6UtX+gH2ZVx1pS9d6QfYl6GNxcVzkiRpdYzLULwkSVoFBrskSR0ytcGe5L8k+UKSW5P8ZZL1C9TbmuSLSfYm2bnW7VyOJOcnuT3Jj5Ms+NWKJHcnuS3JZ5OM3d19jqIfk7BPTkiyJ8ld7eeGBer9qO2PzyYZqwtGl3qekzw8yQfa8puSzKx9K5e2jH68LMn+vv3wm6No51KSXJbkgSSfX2B5kry99fPWJKetdRuXaxl9eV6SB/v2ye+vdRuXI8nmJDckubO9d712QJ213y9VNZUP4GxgXZt+C/CWAXWOA74EPBk4Hvgc8PRRt31AO59G74Y9HwdmF6l3N3DiqNs7TD8maJ/8EbCzTe8c9Ppqy74z6rau9HkGXgX8aZu+APjAqNu9wn68DHjHqNu6jL48FzgN+PwCy88FPkrvviBnAjeNus1D9OV5wEdG3c5l9ONk4LQ2/Rjg/w54fa35fpnaM/aq+lhVHWqzN9L77vzhTgf2VtWXq+rvgPcD29aqjctVVXdW1WrehW8kltmPidgn9Np0eZu+HDhvhG1ZieU8z/19vAo4K8mgm02N0qS8XpZUVZ8EDixSZRvw3uq5EVif5OS1ad3RWUZfJkJV3V9Vn27TDwF3ApsOq7bm+2Vqg/0w/5reEdXhNgH39s3v48idNkkK+FiSW9rteSfRpOyTJ1TV/dD74wdOWqDeI5LMJbkxyTiF/3Ke55/UaQfJDwKPX5PWLd9yXy+/3oZJr0qyecDySTApfxvL9awkn0vy0STPGHVjltI+inomcNNhi9Z8v4zF99iPlST/G3jigEW/V1VXtzq/BxwCrhi0iQFlI/l+4HL6sgzPqar7kpwE7EnyhXbkvGZWoR8TsU+OYjO/0PbJk4Hrk9xWVV9anRYOZTnP89jsi0Usp43/C3hfVf0gySvojUL86jFv2eqbhP2xXJ+md1/07yQ5F/gwsGXEbVpQkkcDHwR+q6q+ffjiAasc0/3S6WCvql9bbHmS7cALgbOqfRhymLG51e1SfVnmNu5rPx9I8pf0hinXNNhXoR8TsU+SfD3JyVV1fxt2e2CBbczvky8n+Ti9I/5xCPblPM/zdfYlWQc8jvEbXl2yH1X1zb7Z/07vmptJNDZ/G8PqD8equibJO5OcWFVj989hkjyMXqhfUVUfGlBlzffL1A7FJ9kK/A7woqr63gLVOnOr2ySPSvKY+Wl6Fw8OvCJ1zE3KPtkFbG/T24EjRiOSbEjy8DZ9IvAc4I41a+HilvM89/fxxcD1Cxwgj9KS/Tjs884X0fucdBLtAi5sV2GfCTw4/3HQpEnyxPnrNZKcTi+rvrn4WmuvtfFS4M6q+uMFqq39fhn1VYWjegB76X3u8dn2mL+690nANX31zqV3peOX6A0Xj7ztA/ryz+gdFf4A+Dpw7eF9oXdV8Ofa4/Zx7Mty+jFB++TxwHXAXe3nCa18Fnh3m342cFvbJ7cBF4263Yf14YjnGXgDvYNhgEcAf9H+lj4FPHnUbV5hP/6w/U18DrgBeOqo27xAP94H3A/8sP2dXAS8AnhFWx7gT1o/b2ORb8iM+rGMvrymb5/cCDx71G1eoB//mN6w+q19WXLuqPeLt5SVJKlDpnYoXpKkLjLYJUnqEINdkqQOMdglSeoQg12SpA4x2CVJ6hCDXZKkDvn/ixWdWCW3RFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_difference(Non_Brexit_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 0\n",
    "partylist = ['9','8','8a','3','2']\n",
    "\n",
    "for item in partylist:\n",
    "    party_similarity(party_df['MemberParty'+item])\n",
    "    iu1 = np.triu_indices(639) \n",
    "\n",
    "    ps_b = pd.DataFrame(1-pairwise_distances(Brexit_table, metric =\"cosine\"), columns=MPs, index=MPs).round(3) - party_sim\n",
    "    b_triangle_upper = ps_b.values[iu1]\n",
    "\n",
    "    ps_nb = pd.DataFrame(1-pairwise_distances(Non_Brexit_table, metric =\"cosine\"), columns=MPs, index=MPs).round(3) - party_sim\n",
    "    nb_triangle_upper = ps_nb.values[iu1]\n",
    "\n",
    "    plt.figure(figsize=(10, 20))\n",
    "    plt.subplot(5,2,a+1)\n",
    "    plt.hist(b_triangle_upper, range=[-2, 2], bins=50, alpha=0.5, label='Brexit')\n",
    "    plt.hist(nb_triangle_upper, range=[-2, 2], bins=50, alpha=0.5, label='Non-Brexit')\n",
    "    plt.title('Histogram of '+item+ ' political parties')\n",
    "    plt.ylabel('Number of MP links')\n",
    "    plt.xlabel('Party-adjusted voting similarity score')\n",
    "    plt.legend(loc='upper right')\n",
    "\n",
    "    plt.subplot(5,2,a+2)\n",
    "    plt.hist(b_triangle_upper, range=[-2, 2], bins=50, alpha=0.5, label='brexit', log = True)\n",
    "    plt.hist(nb_triangle_upper, range=[-2, 2], bins=50, alpha=0.5, label='nonbrexit', log = True)\n",
    "    plt.title('Histogram of '+item+ ' political parties, \\n log scale')\n",
    "    plt.xlabel('Party-adjusted voting similarity score')\n",
    "    plt.ylabel('Count of MP links')\n",
    "    a =+ a\n",
    "    \n",
    "    #plt.savefig(item+'.png', dpi=300)\n",
    "    \n",
    "    #ps_b.to_csv(item+\"_parties_brexit.csv\")\n",
    "    #ps_nb.to_csv(item+\"_parties_nonbrexit.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <th>Adam Holloway</th>\n",
       "      <th>Afzal Khan</th>\n",
       "      <th>Alan Brown</th>\n",
       "      <th>Alan Mak</th>\n",
       "      <th>Albert Owen</th>\n",
       "      <th>Alberto Costa</th>\n",
       "      <th>Alec Shelbrooke</th>\n",
       "      <th>Alex Burghart</th>\n",
       "      <th>Alex Chalk</th>\n",
       "      <th>...</th>\n",
       "      <th>Victoria Prentis</th>\n",
       "      <th>Wayne David</th>\n",
       "      <th>Wendy Morton</th>\n",
       "      <th>Wera Hobhouse</th>\n",
       "      <th>Wes Streeting</th>\n",
       "      <th>Will Quince</th>\n",
       "      <th>Yasmin Qureshi</th>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <th>Yvonne Fovargue</th>\n",
       "      <th>Zac Goldsmith</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.130</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>0.112</td>\n",
       "      <td>-0.285</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>-0.146</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.189</td>\n",
       "      <td>0.139</td>\n",
       "      <td>-0.175</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.129</td>\n",
       "      <td>-0.087</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.232</td>\n",
       "      <td>-0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>0.165</td>\n",
       "      <td>-0.369</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>-0.214</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.263</td>\n",
       "      <td>0.191</td>\n",
       "      <td>-0.265</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.184</td>\n",
       "      <td>-0.149</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.289</td>\n",
       "      <td>-0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afzal Khan</th>\n",
       "      <td>0.136</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.886</td>\n",
       "      <td>0.192</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.253</td>\n",
       "      <td>1.838</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.178</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Brown</th>\n",
       "      <td>0.130</td>\n",
       "      <td>0.169</td>\n",
       "      <td>1.886</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1.906</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.243</td>\n",
       "      <td>...</td>\n",
       "      <td>0.187</td>\n",
       "      <td>1.882</td>\n",
       "      <td>0.257</td>\n",
       "      <td>1.911</td>\n",
       "      <td>1.911</td>\n",
       "      <td>0.171</td>\n",
       "      <td>1.880</td>\n",
       "      <td>1.874</td>\n",
       "      <td>1.795</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan Mak</th>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.155</td>\n",
       "      <td>-0.163</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>0.195</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.184</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.251</td>\n",
       "      <td>-0.104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 639 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Adam Afriyie  Adam Holloway  Afzal Khan  Alan Brown  Alan Mak  \\\n",
       "Adam Afriyie          0.000         -0.085       0.136       0.130    -0.150   \n",
       "Adam Holloway        -0.085          0.000       0.188       0.169    -0.221   \n",
       "Afzal Khan            0.136          0.188       0.000       1.886     0.192   \n",
       "Alan Brown            0.130          0.169       1.886       0.000     0.180   \n",
       "Alan Mak             -0.150         -0.221       0.192       0.180     0.000   \n",
       "\n",
       "               Albert Owen  Alberto Costa  Alec Shelbrooke  Alex Burghart  \\\n",
       "Adam Afriyie         0.112         -0.285           -0.194         -0.146   \n",
       "Adam Holloway        0.165         -0.369           -0.264         -0.214   \n",
       "Afzal Khan          -0.065          0.321            0.209          0.186   \n",
       "Alan Brown           1.906          0.284            0.189          0.179   \n",
       "Alan Mak             0.155         -0.163           -0.112         -0.035   \n",
       "\n",
       "               Alex Chalk      ...        Victoria Prentis  Wayne David  \\\n",
       "Adam Afriyie       -0.227      ...                  -0.189        0.139   \n",
       "Adam Holloway      -0.290      ...                  -0.263        0.191   \n",
       "Afzal Khan          0.250      ...                   0.228       -0.015   \n",
       "Alan Brown          0.243      ...                   0.187        1.882   \n",
       "Alan Mak           -0.095      ...                  -0.094        0.195   \n",
       "\n",
       "               Wendy Morton  Wera Hobhouse  Wes Streeting  Will Quince  \\\n",
       "Adam Afriyie         -0.175          0.202          0.129       -0.087   \n",
       "Adam Holloway        -0.265          0.233          0.184       -0.149   \n",
       "Afzal Khan            0.253          1.838         -0.036        0.178   \n",
       "Alan Brown            0.257          1.911          1.911        0.171   \n",
       "Alan Mak             -0.088          0.259          0.184       -0.072   \n",
       "\n",
       "               Yasmin Qureshi  Yvette Cooper  Yvonne Fovargue  Zac Goldsmith  \n",
       "Adam Afriyie            0.152          0.157            0.232         -0.053  \n",
       "Adam Holloway           0.190          0.201            0.289         -0.099  \n",
       "Afzal Khan             -0.034         -0.034           -0.100          0.145  \n",
       "Alan Brown              1.880          1.874            1.795          0.127  \n",
       "Alan Mak                0.211          0.210            0.251         -0.104  \n",
       "\n",
       "[5 rows x 639 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps_b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Sources\n",
    "https://www.theguardian.com/politics/2018/nov/12/brexit-may-plan-through-house-of-commons\n",
    "http://techinpink.com/2017/08/04/implementing-similarity-measures-cosine-similarity-versus-jaccard-similarity/\n",
    "https://towardsdatascience.com/overview-of-text-similarity-metrics-3397c4601f50"
   ]
  }
 ],
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